Stream Clustering


Data stream clustering aims to find and maintain a set of valid clusters within a continuous and possibly unbounded stream of observations. This website serves as a repository for algorithms, literature and data sets in the field of data stream clustering. Most importantly, we provide a curated list of stream clustering algorithms and corresponding published work.

Publications

This website is accompanying our extensive survey article on stream clustering, published in the BISE Journal:





Tools & Implementations

  • stream (GitHub)

    An R-Package that provides functionality to read, create and analyse data streams in the statistical programming language R. It provides implementations for D-Stream, D-Stream with Attraction, DBSTREAM, BICO and BIRCH.

  • Massive Online Analysis (MOA) (GitHub)

    An open source Java framework for data stream mining. The framework provides methods to analyse data streams, including classification, regression and clustering algorithms. In particular, the following stream clustering algorithms are available: StreamKM++, CluStream, ClusTree, DenStream and D-Stream, BICO and COBWEB.

  • streamMOA (GitHub)

    An R-Package that extends the stream package with interfaces to the MOA framework. In particular, interfaces to the Java implementations of CluStream, DenStream and ClusTree are available.

  • subspaceMOA

    A Java framework for working with data in high-dimensional space. It implements the algorithms PreDeConStream and HDDStream.

  • subspaceMOA (GitHub)

    A R-Package that interfaces the subspaceMOA Java framework. It provides interfaces for PreDeConStream and HDDStream.

  • streamDM (GitHub)

    Open source software for mining data stream in Spark Streaming. Spark streaming is an extension to the Spark engine. It implements the CluStream, streamKM++ and DenStream algorithm.


  • evoStream

    Official implementation of an evolutionary stream clustering algorithm. The algorithm is able to utilize the idle time in a stream in order to incrementally improve the clustering result. Ports for C++ and Python are also available

  • textClust

    R-Package which implements an algorithm to cluster text streams.

  • BIRCH

    Official C implementation from the authors. Not compatible with Windows. A Python implementation is also available in scikit-learn.

  • STREAM

    Official C++ implementation of the local search procedure used in STREAM. Extensions to the streaming case are provided by the authors of BICO and StreamKM++. Both extensions do not include the incremental reclustering when memory is exceeded.

  • BICO

    Official C++ implementation from the authors. There is also a Python port available here as well as an R port in the stream package as well as a Java port in the MOA Framework

  • StreamKM++

    Official C++ implementation from the authors.

  • RepStream

    Previously available C++ implementation from the authors. Still accessible using the Internet Archive.


Datasets

  • Sensor

    This data stream contains sensor readings from 37 days within the Intel Berkley Research Lab. In total 54 sensors were deployed that recorded the temperature, light, humidty, and voltage in the stream. In total, the stream contains 2,219,803 observations in 4 dimension. As a class label, the ID of the sensor that read the value is commonly used. The data set shows day-night cycles where the light and temperature tends to be higher during working hours.

  • KDDCup '99

    This data stream was created for the Third International Knowledge Discovery and Data Mining Tools Competition. It contains 4,898,431 records of network traffic data. In total 41 features are available which describe the connection, e.g. the protocol type or duration of the connection. 31 of the available features are numeric. In addition, a class label describes whether the connection is normal or one of 22 types of attack on the network. Note, that the data set is considered outdated for network intrusion detection. However, its popularity within the pattern recognition community remains.

  • Powersupply

    This stream contains the hourly power supply of an Italian electricity supplier. The stream contains a total of 29,928 observations. Each hour two features are recorded, namely the power supply of the main grid and other grids.

  • Data Stream Repository

    Collection of many popular data streams (including the ones mentioned above).

  • UCI Machine Learning Repository

    A collection of 394 datasets for machine learning purposes. Particularly popular for clustering is the Covertype dataset. It contains a total of 581,012 observations. Each observation describes a 30x30 metre area of forest. The 13 features contain cartographic information such as shade, slop or elevation. 10 of these features are numeric. The 3 categorial features are often dummy encoded into binary variables. The class label indicates one of seven cover types.

  • Clustering Benchmarks

    Collection of popular clustering data sets.


Algorithms & Literature


Textbooks


Surveys

Ghesmoune M, Lebbah M and Azzag H (2016), "State-of-the-art on clustering data streams", Big Data Analytics. Vol. 1(1), pp. 13.
[BibTeX] [DOI]
@article{GhesmouneLebbahAzzag2016,
	author = {Ghesmoune, Mohammed and Lebbah, Mustapha and Azzag, Hanene},
	title = {State-of-the-art on clustering data streams},
	journal = {Big Data Analytics},
	year = {2016},
	volume = {1},
	number = {1},
	pages = {13},
	doi = {10.1186/s41044-016-0011-3}
}
Silva JA, Faria ER, Barros RC, Hruschka ER, Carvalho ACPLFd and Gama Ja (2013), "Data Stream Clustering: A Survey", ACM Comput. Surv.. New York, NY, USA, 7, 2013. Vol. 46(1), pp. 13:1-13:31. ACM.
[BibTeX] [DOI]
@article{SilvaFariaBarrosEtAl2013,
	author = {Silva, Jonathan A. and Faria, Elaine R. and Barros, Rodrigo C. and Hruschka, Eduardo R. and Carvalho, André C. P. L. F. de and Gama, João},
	title = {Data Stream Clustering: A Survey},
	journal = {ACM Comput. Surv.},
	publisher = {ACM},
	year = {2013},
	volume = {46},
	number = {1},
	pages = {13:1--13:31},
	doi = {10.1145/2522968.2522981}
}
Amini A, Wah TY and Saboohi H (2014), "On Density-Based Data Streams Clustering Algorithms: A Survey", Journal of Computer Science and Technology. Vol. 29(1), pp. 116-141.
[BibTeX] [DOI]
@article{AminiWahSaboohi2014,
	author = {Amini, Amineh and Wah, Teh Ying and Saboohi, Hadi},
	title = {On Density-Based Data Streams Clustering Algorithms: A Survey},
	journal = {Journal of Computer Science and Technology},
	year = {2014},
	volume = {29},
	number = {1},
	pages = {116--141},
	doi = {10.1007/s11390-014-1416-y}
}
Nguyen H-L, Woon Y-K and Ng W-K (2015), "A survey on data stream clustering and classification", Knowledge and Information Systems. Vol. 45(3), pp. 535-569.
[BibTeX] [DOI]
@article{NguyenWoonNg2015,
  author = {Nguyen, Hai-Long and Woon, Yew-Kwong and Ng, Wee-Keong},
  title = {A survey on data stream clustering and classification},
  journal = {Knowledge and Information Systems},
  year = {2015},
  volume = {45},
  number = {3},
  pages = {535--569},
  doi = {10.1007/s10115-014-0808-1}
}
Mousavi M, Bakar AzuralizaAbu and Vakilian M (2015), "Data stream clustering algorithms: A review", International Journal of Advances in Soft Computing and its Applications. Vol. 7, pp. 1-15. International Center for Scientific Research and Studies (ICSRS).
[BibTeX]
@article{MousaviBakarVakilian2015,
  author = {Maryam Mousavi and Bakar, Azuraliza Abu and Mohammadmahdi Vakilian},
  title = {Data stream clustering algorithms: A review},
  journal = {International Journal of Advances in Soft Computing and its Applications},
  publisher = {International Center for Scientific Research and Studies (ICSRS)},
  year = {2015},
  volume = {7},
  pages = {1--15}
}
Amini A and Wah TY (2012), "A Comparative Study of Density-based Clustering Algorithms on Data Streams: Micro-clustering Approaches", In Intelligent Control and Innovative Computing. Boston, MA , pp. 275-287. Springer US.
[BibTeX] [DOI]
@inbook{AminiWah2012,
	author = {Amini, Amineh and Wah, Teh Ying},
	editor = {Ao, Sio Iong and Castillo, Oscar and Huang, Xu},
	title = {A Comparative Study of Density-based Clustering Algorithms on Data Streams: Micro-clustering Approaches},
	booktitle = {Intelligent Control and Innovative Computing},
	publisher = {Springer US},
	year = {2012},
	pages = {275--287},
	doi = {10.1007/978-1-4614-1695-1_21}
}
Amini A and Wah TY (2011), "Density micro-clustering algorithms on data streams: A review", In Proceeding of the International Multiconference of Engineers and Computer scientists (IMECS).
[BibTeX]
@inproceedings{AminiWah2011,
  author = {Amini, Amineh and Wah, Teh Ying},
  title = {Density micro-clustering algorithms on data streams: A review},
  booktitle = {Proceeding of the International Multiconference of Engineers and Computer scientists (IMECS)},
  year = {2011}
}
Amini A, Wah TY, Saybani MR and Yazdi SRAS (2011), "A study of density-grid based clustering algorithms on data streams", In Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)., 7, 2011. Vol. 3, pp. 1652-1656.
[BibTeX] [DOI]
@inproceedings{AminiWahSaybaniEtAl2011,
  author = {A. Amini and T. Y. Wah and M. R. Saybani and S. R. A. S. Yazdi},
  title = {A study of density-grid based clustering algorithms on data streams},
  booktitle = {Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)},
  year = {2011},
  volume = {3},
  pages = {1652--1656},
  doi = {10.1109/FSKD.2011.6019867}
}
Ma WH (2014), "Survey on Data Streams Clustering Techniques", In Manufacture Engineering, Quality and Production System III., 7, 2014. Vol. 933, pp. 768-773. Trans Tech Publications.
[BibTeX] [DOI]
@inproceedings{Ma2014,
  author = {Ma, Wei Hua},
  title = {Survey on Data Streams Clustering Techniques},
  booktitle = {Manufacture Engineering, Quality and Production System III},
  publisher = {Trans Tech Publications},
  year = {2014},
  volume = {933},
  pages = {768--773},
  doi = {10.4028/www.scientific.net/AMR.933.768}
}
Mansalis S, Ntoutsi E, Pelekis N and Theodoridis Y (2018), "An evaluation of data stream clustering algorithms", Statistical Analysis and Data Mining: The ASA Data Science Journal. Vol. 11(4), pp. 167-187.
[BibTeX] [DOI]
@article{MansalisNtoutsiPelekisEtAl2018,
  author = {Mansalis, Stratos and Ntoutsi, Eirini and Pelekis, Nikos and Theodoridis, Yannis},
  title = {An evaluation of data stream clustering algorithms},
  journal = {Statistical Analysis and Data Mining: The ASA Data Science Journal},
  year = {2018},
  volume = {11},
  number = {4},
  pages = {167-187},
  doi = {10.1002/sam.11380}
}
Kokate U, Deshpande A, Mahalle P and Patil P (2018), "Data Stream Clustering Techniques, Applications, and Models: Comparative Analysis and Discussion", Big Data and Cognitive Computing., oct, 2018. Vol. 2(4), pp. 32. MDPI AG.
[BibTeX] [DOI]
@article{KokateDeshpandeMahalleEtAl2018,
  author = {Umesh Kokate and Arvind Deshpande and Parikshit Mahalle and Pramod Patil},
  title = {Data Stream Clustering Techniques, Applications, and Models: Comparative Analysis and Discussion},
  journal = {Big Data and Cognitive Computing},
  publisher = {MDPI AG},
  year = {2018},
  volume = {2},
  number = {4},
  pages = {32},
  doi = {10.3390/bdcc2040032}
}

Empirical Evaluations

Carnein M, Assenmacher D and Trautmann H (2017), "An Empirical Comparison of Stream Clustering Algorithms", In Proceedings of the ACM International Conference on Computing Frontiers (CF '17). Siena, Italy , pp. 361 - 365.
[Paper] [BibTeX] [DOI]
@inproceedings{CarneinAssenmacherTrautmann2017,
	author = {Matthias Carnein AND Dennis Assenmacher AND Heike Trautmann},
	title = {An Empirical Comparison of Stream Clustering Algorithms},
	booktitle = {Proceedings of the ACM International Conference on Computing Frontiers (CF '17)},
	year = {2017},
	pages = {361 -- 365},
	doi = {10.1145/3075564.3078887}
}

Distance-Based Algorithms

Clustering Feature

BIRCH
Zhang T, Ramakrishnan R and Livny M (1996), "BIRCH: An Efficient Data Clustering Method for Very Large Databases", In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data. Montreal, Quebec, Canada , pp. 103-114. ACM.
[BibTeX] [DOI]
@inproceedings{ZhangRamakrishnanLivny1996,
  author = {Zhang, Tian and Ramakrishnan, Raghu and Livny, Miron},
  title = {BIRCH: An Efficient Data Clustering Method for Very Large Databases},
  booktitle = {Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data},
  publisher = {ACM},
  year = {1996},
  pages = {103--114},
  doi = {10.1145/233269.233324}
}
Zhang T, Ramakrishnan R and Livny M (1997), "BIRCH: A new data clustering algorithm and its applications", Data Mining and Knowledge Discovery. Vol. 1(2), pp. 141-182.
[BibTeX]
@article{ZhangRamakrishnanLivny1997,
  author = {Tian Zhang and Raghu Ramakrishnan and Miron Livny},
  title = {BIRCH: A new data clustering algorithm and its applications},
  journal = {Data Mining and Knowledge Discovery},
  year = {1997},
  volume = {1},
  number = {2},
  pages = {141--182},
}
Improved BIRCH
A. I. McLeod DRB (1983), "A Convenient Algorithm for Drawing a Simple Random Sample", Journal of the Royal Statistical Society. Series C (Applied Statistics). Vol. 32(2), pp. 182-184. Wiley, Royal Statistical Society.
[BibTeX]
@article{A.I.McLeod1983,
  author = {A. I. McLeod, D. R. Bellhouse},
  title = {A Convenient Algorithm for Drawing a Simple Random Sample},
  journal = {Journal of the Royal Statistical Society. Series C (Applied Statistics)},
  publisher = {Wiley, Royal Statistical Society},
  year = {1983},
  volume = {32},
  number = {2},
  pages = {182-184}
}
A-BIRCH
Lorbeer B, Kosareva A, Deva B, Softić Dž, Ruppel P and Küpper A (2017), "A-BIRCH: Automatic Threshold Estimation for the BIRCH Clustering Algorithm", In Advances in Big Data: Proceedings of the 2nd INNS Conference on Big Data. Thessaloniki, Greece , pp. 169-178. Springer International Publishing.
[BibTeX] [DOI]
@inbook{LorbeerKosarevaDevaEtAl2017,
	author = {Lorbeer, Boris and Kosareva, Ana and Deva, Bersant and Softić, Dženan and Ruppel, Peter and Küpper, Axel},
	editor = {Angelov, Plamen and Manolopoulos, Yannis and Iliadis, Lazaros and Roy, Asim and Vellasco, Marley},
	title = {A-BIRCH: Automatic Threshold Estimation for the BIRCH Clustering Algorithm},
	booktitle = {Advances in Big Data: Proceedings of the 2nd INNS Conference on Big Data},
	publisher = {Springer International Publishing},
	year = {2017},
	pages = {169--178},
	doi = {10.1007/978-3-319-47898-2_18}
}
ScaleKM
Bradley P, Fayyad U and Reina C (1998), "Scaling Clustering Algorithms to Large Databases", In Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD'98). , pp. 9-15. AAAI Press.
[BibTeX]
@inproceedings{BradleyFayyadReina1998,
  author = {P.S. Bradley and Usama Fayyad and Cory Reina},
  title = {Scaling Clustering Algorithms to Large Databases},
  booktitle = {Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD'98)},
  publisher = {AAAI Press},
  year = {1998},
  pages = {9--15}
}
Single-pass k-means
Farnstrom F, Lewis J and Elkan C (2000), "Scalability for Clustering Algorithms Revisited", SIGKDD Explor. Newsl.. New York, NY, USA, 1, 2000. Vol. 2(1), pp. 51-57. ACM.
[BibTeX] [DOI]
@article{FarnstromLewisElkan2000,
  author = {Farnstrom, Fredrik and Lewis, James and Elkan, Charles},
  title = {Scalability for Clustering Algorithms Revisited},
  journal = {SIGKDD Explor. Newsl.},
  publisher = {ACM},
  year = {2000},
  volume = {2},
  number = {1},
  pages = {51--57},
  doi = {10.1145/360402.360419}
}
ACSC
Fahy C, Yang S and Gongora M (2018), "Ant Colony Stream Clustering: A Fast Density Clustering Algorithm for Dynamic Data Streams", IEEE Transactions on Cybernetics. , pp. 1-14.
[BibTeX] [DOI]
@article{FahyYangGongora2018,
  author = {C. Fahy and S. Yang and M. Gongora},
  title = {Ant Colony Stream Clustering: A Fast Density Clustering Algorithm for Dynamic Data Streams},
  journal = {IEEE Transactions on Cybernetics},
  year = {2018},
  pages = {1--14},
  doi = {10.1109/TCYB.2018.2822552}
}

Extended Clustering Feature

CluStream
Aggarwal CC, Han J, Wang J and Yu PS (2003), "A Framework for Clustering Evolving Data Streams", In Proceedings of the 29th International Conference on Very Large Data Bases. Berlin, Germany Vol. 29, pp. 81-92. VLDB Endowment.
[BibTeX]
@inproceedings{AggarwalHanWangEtAl2003,
  author = {Aggarwal, Charu C. and Han, Jiawei and Wang, Jianyong and Yu, Philip S.},
  title = {A Framework for Clustering Evolving Data Streams},
  booktitle = {Proceedings of the 29th International Conference on Very Large Data Bases},
  publisher = {VLDB Endowment},
  year = {2003},
  volume = {29},
  pages = {81--92}
}
HCluStream
Yang C and Zhou J (2006), "HClustream: A Novel Approach for Clustering Evolving Heterogeneous Data Stream", In Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)., 12, 2006. , pp. 682-688.
[BibTeX] [DOI]
@inproceedings{YangZhou2006,
  author = {C. Yang and J. Zhou},
  title = {HClustream: A Novel Approach for Clustering Evolving Heterogeneous Data Stream},
  booktitle = {Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)},
  year = {2006},
  pages = {682--688},
  doi = {10.1109/ICDMW.2006.89}
}
SWClustering
Zhou A, Cao F, Qian W and Jin C (2007), "Tracking clusters in evolving data streams over sliding windows", Knowledge and Information Systems. Vol. 15(2), pp. 181-214.
[BibTeX] [DOI]
@article{ZhouCaoQianEtAl2007,
  author = {Zhou, Aoying and Cao, Feng and Qian, Weining and Jin, Cheqing},
  title = {Tracking clusters in evolving data streams over sliding windows},
  journal = {Knowledge and Information Systems},
  year = {2007},
  volume = {15},
  number = {2},
  pages = {181--214},
  doi = {10.1007/s10115-007-0070-x}
}
SDStream
Ren J and Ma R (2009), "Density-Based Data Streams Clustering over Sliding Windows", In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery., 8, 2009. Vol. 5, pp. 248-252.
[BibTeX] [DOI]
@inproceedings{RenMa2009,
  author = {J. Ren and R. Ma},
  title = {Density-Based Data Streams Clustering over Sliding Windows},
  booktitle = {2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery},
  year = {2009},
  volume = {5},
  pages = {248--252},
  doi = {10.1109/FSKD.2009.553}
}

Time-Faded Clustering Feature

DenStream
Cao F, Ester M, Qian W and Zhou A (2006), "Density-based clustering over an evolving data stream with noise", In Conference on Data Mining (SIAM '06). , pp. 328-339.
[BibTeX] [DOI]
@inproceedings{CaoEsterQianEtAl2006,
  author = {Feng Cao and Martin Ester and Weining Qian and Aoying Zhou},
  title = {Density-based clustering over an evolving data stream with noise},
  booktitle = {Conference on Data Mining (SIAM '06)},
  year = {2006},
  pages = {328--339},
  doi = {10.1109/CCCM.2009.5267735}
}
C-DenStream
Ruiz C, Menasalvas E and Spiliopoulou M (2009), "C-DenStream: Using Domain Knowledge on a Data Stream", In Discovery Science: 12th International Conference, DS 2009, Porto, Portugal, October 3-5, 2009. Berlin, Heidelberg , pp. 287-301. Springer Berlin Heidelberg.
[BibTeX] [DOI]
@inbook{RuizMenasalvasSpiliopoulou2009,
  author = {Ruiz, Carlos and Menasalvas, Ernestina and Spiliopoulou, Myra},
  editor = {Gama, João and Costa, Vítor Santos and Jorge, Alípio Mário and Brazdil, Pavel B.},
  title = {C-DenStream: Using Domain Knowledge on a Data Stream},
  booktitle = {Discovery Science: 12th International Conference, DS 2009, Porto, Portugal, October 3-5, 2009},
  publisher = {Springer Berlin Heidelberg},
  year = {2009},
  pages = {287--301},
  doi = {10.1007/978-3-642-04747-3_23}
}
rDenStream
x. Liu L, Huang H, f. Guo Y and c. Chen F (2009), "rDenStream, A Clustering Algorithm over an Evolving Data Stream", In 2009 International Conference on Information Engineering and Computer Science., 12, 2009. , pp. 1-4.
[BibTeX] [DOI]
@inproceedings{LiuHuangGuoEtAl2009,
  author = {L. x. Liu and H. Huang and Y. f. Guo and F. c. Chen},
  title = {rDenStream, A Clustering Algorithm over an Evolving Data Stream},
  booktitle = {2009 International Conference on Information Engineering and Computer Science},
  year = {2009},
  pages = {1-4},
  doi = {10.1109/ICIECS.2009.5363379}
}
HDenStream
Lin J and Lin H (2009), "A density-based clustering over evolving heterogeneous data stream", In 2009 ISECS International Colloquium on Computing, Communication, Control, and Management., 8, 2009. Vol. 4, pp. 275-277.
[BibTeX] [DOI]
@inproceedings{LinLin2009,
  author = {J. Lin and H. Lin},
  title = {A density-based clustering over evolving heterogeneous data stream},
  booktitle = {2009 ISECS International Colloquium on Computing, Communication, Control, and Management},
  year = {2009},
  volume = {4},
  pages = {275--277},
  doi = {10.1109/CCCM.2009.5267735}
}
E-Stream
Udommanetanakit K, Rakthanmanon T and Waiyamai K (2007), "E-Stream: Evolution-Based Technique for Stream Clustering", In Proceedings of the Third International Conference on Advanced Data Mining and Applications (ADMA '07). Berlin, Heidelberg , pp. 605-615. Springer Berlin Heidelberg.
[BibTeX] [DOI]
@inbook{UdommanetanakitRakthanmanonWaiyamai2007,
  author = {Komkrit Udommanetanakit AND Rakthanmanon, Thanawin AND Waiyamai, Kitsana},
  editor = {Alhajj, Reda and Gao, Hong and Li, Jianzhong and Li, Xue and Zaïane, Osmar R.},
  title = {E-Stream: Evolution-Based Technique for Stream Clustering},
  booktitle = {Proceedings of the Third International Conference on Advanced Data Mining and Applications (ADMA '07)},
  publisher = {Springer Berlin Heidelberg},
  year = {2007},
  pages = {605--615},
  doi = {10.1007/978-3-540-73871-8_58}
}
HUE-Stream
Meesuksabai W, Kangkachit T and Waiyamai K (2011), "HUE-Stream: Evolution-Based Clustering Technique for Heterogeneous Data Streams with Uncertainty.", In ADMA. Vol. 7121, pp. 27-40. Springer.
[BibTeX] [DOI]
@inproceedings{Meesuksabai2011,
  author = {Meesuksabai, Wicha and Kangkachit, Thanapat and Waiyamai, Kitsana},
  editor = {Tang, Jie and King, Irwin and Chen, Ling and Wang, Jianyong},
  title = {HUE-Stream: Evolution-Based Clustering Technique for Heterogeneous Data Streams with Uncertainty.},
  booktitle = {ADMA},
  publisher = {Springer},
  year = {2011},
  volume = {7121},
  pages = {27--40},
  doi = {10.1007/978-3-642-25856-5_3}
}
ClusTree
Kranen P, Assent I, Baldauf C and Seidl T (2009), "Self-Adaptive Anytime Stream Clustering", In Ninth IEEE International Conference on Data Mining (ICDM '09)., 12, 2009. , pp. 249-258.
[BibTeX] [DOI]
@inproceedings{KranenAssentBaldaufEtAl2009,
  author = {Kranen, Philipp and Assent, Ira and Baldauf, Corinna and Seidl, Thomas},
  title = {Self-Adaptive Anytime Stream Clustering},
  booktitle = {Ninth IEEE International Conference on Data Mining (ICDM '09)},
  year = {2009},
  pages = {249--258},
  doi = {10.1109/ICDM.2009.47}
}
LiarTree
Hassani M, Kranen P and Seidl T (2011), "Precise Anytime Clustering of Noisy Sensor Data with Logarithmic Complexity", In Proc. 5th International Workshop on Knowledge Discovery from Sensor Data (SensorKDD 2011) in conjunction with 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2011), San Diego, CA, USA. NewYork, NY, USA , pp. 52-60. ACM.
[BibTeX]
@inproceedings{Hassani2011,
  author = {Marwan Hassani and Philipp Kranen and Thomas Seidl},
  title = {Precise Anytime Clustering of Noisy Sensor Data with Logarithmic Complexity},
  booktitle = {Proc. 5th International Workshop on Knowledge Discovery from Sensor Data (SensorKDD 2011) in conjunction with 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2011), San Diego, CA, USA},
  publisher = {ACM},
  year = {2011},
  pages = {52--60}
}
Kranen P, Reidl F, Sanchez Villaamil F and Seidl T (2011), "Hierarchical Clustering for Real-Time Stream Data with Noise", In Scientific and Statistical Database Management: 23rd International Conference, SSDBM 2011, Portland, OR, USA, July 20-22, 2011. Proceedings. Berlin, Heidelberg , pp. 405-413. Springer Berlin Heidelberg.
[BibTeX] [DOI]
@InBook{Kranen2011,
  author = {Kranen, Philipp and Reidl, Felix and Sanchez Villaamil, Fernando and Seidl, Thomas},
  title = {Hierarchical Clustering for Real-Time Stream Data with Noise},
  booktitle = {Scientific and Statistical Database Management: 23rd International Conference, SSDBM 2011, Portland, OR, USA, July 20-22, 2011. Proceedings},
  year = {2011},
  editor = {Bayard Cushing, Judith and French, James and Bowers, Shawn},
  publisher = {Springer Berlin Heidelberg},
  isbn = {978-3-642-22351-8},
  pages = {405--413},
  doi = {10.1007/978-3-642-22351-8_25},
  address = {Berlin, Heidelberg},
}
HCDD
Zgraja J and Woźniak M (2018), "Drifted Data Stream Clustering Based on ClusTree Algorithm", In Hybrid Artificial Intelligent Systems. Cham , pp. 338-349. Springer International Publishing.
[BibTeX]
@inproceedings{ZgrajaWozniak2018,
  author = {Zgraja, Jakub and Woźniak, Michał},
  editor = {de Cos Juez, Francisco Javier and Villar, José Ramón and de la Cal, Enrique A. and Herrero, Álvaro and Quintián, Héctor and Sáez, José António and Corchado, Emilio},
  title = {Drifted Data Stream Clustering Based on ClusTree Algorithm},
  booktitle = {Hybrid Artificial Intelligent Systems},
  publisher = {Springer International Publishing},
  year = {2018},
  pages = {338--349}
}
FlockStream
Forestiero A, Pizzuti C and Spezzano G (2013), "A single pass algorithm for clustering evolving data streams based on swarm intelligence", Data Mining and Knowledge Discovery. Vol. 26(1), pp. 1-26.
[BibTeX] [DOI]
@article{ForestieroPizzutiSpezzano2013,
  author = {Forestiero, Agostino and Pizzuti, Clara and Spezzano, Giandomenico},
  title = {A single pass algorithm for clustering evolving data streams based on swarm intelligence},
  journal = {Data Mining and Knowledge Discovery},
  year = {2013},
  volume = {26},
  number = {1},
  pages = {1--26},
  doi = {10.1007/s10618-011-0242-x}
}
LeaDen-Stream
Amini A and Wah TY (2013), "LeaDen-Stream: A Leader Density-Based Clustering Algorithm over Evolving Data Stream", JCC. Vol. 01(05), pp. 26-31. Scientific Research Publishing, Inc,.
[BibTeX] [DOI]
@article{AminiWah2013,
  author = {Amineh Amini and Teh Ying Wah},
  title = {LeaDen-Stream: A Leader Density-Based Clustering Algorithm over Evolving Data Stream},
  journal = {JCC},
  publisher = {Scientific Research Publishing, Inc,},
  year = {2013},
  volume = {01},
  number = {05},
  pages = {26--31},
  doi = {10.4236/jcc.2013.15005}
}

Medoids

RepStream
Lühr S and Lazarescu M (2009), "Incremental clustering of dynamic data streams using connectivity based representative points", Data & Knowledge Engineering . Vol. 68(1), pp. 1 - 27.
[BibTeX] [DOI]
						@article{LuehrLazarescu2009,
						  author = {Sebastian Lühr and Mihai Lazarescu},
						  title = {Incremental clustering of dynamic data streams using connectivity based representative points},
						  journal = {Data & Knowledge Engineering },
						  year = {2009},
						  volume = {68},
						  number = {1},
						  pages = {1 -- 27},
						  doi = {10.1016/j.datak.2008.08.006}
						}
						
Lühr S and Lazarescu M (2008), "Connectivity Based Stream Clustering Using Localised Density Exemplars", In Advances in Knowledge Discovery and Data Mining: 12th Pacific-Asia Conference, PAKDD '08. Berlin, Heidelberg , pp. 662-672. Springer Berlin Heidelberg.
[BibTeX] [DOI]
						@inbook{LuehrLazarescu2008,
						  author = {Lühr, Sebastian and Lazarescu, Mihai},
						  editor = {Washio, Takashi and Suzuki, Einoshin and Ting, Kai Ming and Inokuchi, Akihiro},
						  title = {Connectivity Based Stream Clustering Using Localised Density Exemplars},
						  booktitle = {Advances in Knowledge Discovery and Data Mining: 12th Pacific-Asia Conference, PAKDD '08},
						  publisher = {Springer Berlin Heidelberg},
						  year = {2008},
						  pages = {662--672},
						  doi = {10.1007/978-3-540-68125-0_62}
						}
						
StreamKM++
Ackermann MR, Märtens M, Raupach C, Swierkot K, Lammersen C and Sohler C (2012), "StreamKM++: A Clustering Algorithm for Data Streams", J. Exp. Algorithmics. New York, NY, USA, 5, 2012. Vol. 17, pp. 2.4:2.1-2.4:2.30. ACM.
[BibTeX] [DOI]
@article{AckermannMaertensRaupachEtAl2012,
  author = {Ackermann, Marcel R. and Märtens, Marcus and Raupach, Christoph and Swierkot, Kamil and Lammersen, Christiane and Sohler, Christian},
  title = {StreamKM++: A Clustering Algorithm for Data Streams},
  journal = {J. Exp. Algorithmics},
  publisher = {ACM},
  year = {2012},
  volume = {17},
  pages = {2.4:2.1--2.4:2.30},
  doi = {10.1145/2133803.2184450}
}
BICO
Fichtenberger H, Gillé M, Schmidt M, Schwiegelshohn C and Sohler C (2013), "BICO: BIRCH Meets Coresets for k-Means Clustering", In Algorithms - ESA 2013 - 21st Annual European Symposium, Sophia Antipolis, France, September 2-4, 2013. Proceedings. , pp. 481-492.
[BibTeX] [DOI]
@inproceedings{FichtenbergerGilleSchmidtEtAl2013,
  author = {Hendrik Fichtenberger and Marc Gillé and Melanie Schmidt and Chris Schwiegelshohn and Christian Sohler},
  title = {BICO: BIRCH Meets Coresets for k-Means Clustering},
  booktitle = {Algorithms - ESA 2013 - 21st Annual European Symposium, Sophia Antipolis, France, September 2-4, 2013. Proceedings},
  year = {2013},
  pages = {481--492},
  doi = {10.1007/978-3-642-40450-4_41}
}

Centroids

STREAM / StreamLS
O'Callaghan L, Mishra N, Meyerson A, Guha S and Motwani R (2002), "Streaming-data algorithms for high-quality clustering", In Proceedings of the 18th International Conference on Data Engineering (ICDE). , pp. 685-694.
[BibTeX] [DOI]
						@inproceedings{OCallaghanMishraMeyersonEtAl2002,
						  author = {O'Callaghan, L. and Mishra, N. and Meyerson, A. and Guha, S. and Motwani, R.},
						  title = {Streaming-data algorithms for high-quality clustering},
						  booktitle = {Proceedings of the 18th International Conference on Data Engineering (ICDE)},
						  year = {2002},
						  pages = {685--694},
						  doi = {10.1109/ICDE.2002.994785}
						}
						
Guha S, Meyerson A, Mishra N, Motwani R and O'Callaghan L (2003), "Clustering data streams: Theory and practice", IEEE Transactions on Knowledge and Data Engineering., 5, 2003. Vol. 15(3), pp. 515-528.
[BibTeX] [DOI]
						@article{GuhaMeyersonMishraEtAl2003,
						  author = {Guha, S. and Meyerson, A. and Mishra, N. and Motwani, R. and O'Callaghan, L.},
						  title = {Clustering data streams: Theory and practice},
						  journal = {IEEE Transactions on Knowledge and Data Engineering},
						  year = {2003},
						  volume = {15},
						  number = {3},
						  pages = {515--528},
						  doi = {10.1109/TKDE.2003.1198387}
						}
						
Olindda
Spinosa EJ, de Leon F de Carvalho AP and Gama J (2007), "Olindda: A cluster-based approach for detecting novelty and concept drift in data streams", In Proceedings of the 2007 ACM symposium on Applied computing. , pp. 448-452.
[BibTeX]
												@inproceedings{SpinosaLeonFdeCarvalhoGama2007,
												  author = {Spinosa, Eduardo J and de Leon F de Carvalho, André Ponce and Gama, João},
												  title = {Olindda: A cluster-based approach for detecting novelty and concept drift in data streams},
												  booktitle = {Proceedings of the 2007 ACM symposium on Applied computing},
												  year = {2007},
												  pages = {448--452}
												}
												

Competitive Learning

SOStream
Isaksson C, Dunham MH and Hahsler M (2012), "SOStream: Self Organizing Density-Based Clustering over Data Stream", In Proceedings of the 8th International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM '12). Berlin, Heidelberg , pp. 264-278. Springer Berlin Heidelberg.
[BibTeX] [DOI]
						@inbook{IsakssonDunhamHahsler2012,
						  author = {Isaksson, Charlie and Dunham, Margaret H. and Hahsler, Michael},
						  editor = {Perner, Petra},
						  title = {SOStream: Self Organizing Density-Based Clustering over Data Stream},
						  booktitle = {Proceedings of the 8th International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM '12)},
						  publisher = {Springer Berlin Heidelberg},
						  year = {2012},
						  pages = {264--278},
						  doi = {10.1007/978-3-642-31537-4_21}
						}
						
DBSTREAM
Hahsler M and Bolaños M (2016), "Clustering Data Streams Based on Shared Density between Micro-Clusters", IEEE Transactions on Knowledge and Data Engineering., 6, 2016. Vol. 28(6), pp. 1449-1461.
[BibTeX] [DOI]
												@article{HahslerBolanos2016,
												  author = {Michael Hahsler and Matthew Bolaños},
												  title = {Clustering Data Streams Based on Shared Density between Micro-Clusters},
												  journal = {IEEE Transactions on Knowledge and Data Engineering},
												  year = {2016},
												  volume = {28},
												  number = {6},
												  pages = {1449--1461},
												  doi = {10.1109/TKDE.2016.2522412}
												}
												
evoStream
Carnein M and Trautmann H (2018), "evoStream --- Evolutionary Stream Clustering Utilizing Idle Times", Big Data Research., 5, 2018. Vol. 14, pp. 101 - 111.
[Paper] [BibTeX] [ DOI]
@article{BDR18,
	author = {Matthias Carnein and Heike Trautmann},
	title = {evoStream --- Evolutionary Stream Clustering Utilizing Idle Times},
	journal = {Big Data Research},
	year = {2018},
	volume = {14},
	pages = {101 -- 111},
	doi = {10.1016/j.bdr.2018.05.005}
}
G-Stream
Ghesmoune M, Azzag H and Lebbah M (2014), "G-Stream: Growing Neural Gas over Data Stream", In Neural Information Processing: 21st International Conference, ICONIP 2014, Kuching, Malaysia, November 3-6, 2014. Proceedings, Part I. , pp. 207-214. Springer International Publishing.
[BibTeX] [DOI]
@inproceedings{GhesmouneAzzagLebbah2014,
  author = {Ghesmoune, Mohammed and Azzag, Hanene and Lebbah, Mustapha},
  editor = {Loo, Chu Kiong and Yap, Keem Siah and Wong, Kok Wai and Teoh, Andrew and Huang, Kaizhu},
  title = {G-Stream: Growing Neural Gas over Data Stream},
  booktitle = {Neural Information Processing: 21st International Conference, ICONIP 2014, Kuching, Malaysia, November 3-6, 2014. Proceedings, Part I},
  publisher = {Springer International Publishing},
  year = {2014},
  pages = {207--214},
  doi = {10.1007/978-3-319-12637-1_26}
}
Ghesmoune M, Lebbah M and Azzag H (2015), "Clustering Over Data Streams Based on Growing Neural Gas", In Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II. , pp. 134-145. Springer International Publishing.
[BibTeX] [DOI]
@inbook{GhesmouneLebbahAzzag2015,
  author = {Ghesmoune, Mohammed and Lebbah, Mustapha and Azzag, Hanene},
  editor = {Cao, Tru and Lim, Ee-Peng and Zhou, Zhi-Hua and Ho, Tu-Bao and Cheung, David and Motoda, Hiroshi},
  title = {Clustering Over Data Streams Based on Growing Neural Gas},
  booktitle = {Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II},
  publisher = {Springer International Publishing},
  year = {2015},
  pages = {134--145},
  doi = {10.1007/978-3-319-18032-8_11}
}

Grid-Based Algorithms

One-Time Partitioning

DUCstream
Gao J, Li J, Zhang Z and Tan P-N (2005), "An Incremental Data Stream Clustering Algorithm Based on Dense Units Detection", In Advances in Knowledge Discovery and Data Mining: 9th Pacific-Asia Conference (PAKDD 2005). Berlin, Heidelberg , pp. 420-425. Springer Berlin Heidelberg.
[BibTeX] [DOI]
@inbook{GaoLiZhangEtAl2005,
  author = {Gao, Jing and Li, Jianzhong and Zhang, Zhaogong and Tan, Pang-Ning},
  editor = {Ho, Tu Bao and Cheung, David and Liu, Huan},
  title = {An Incremental Data Stream Clustering Algorithm Based on Dense Units Detection},
  booktitle = {Advances in Knowledge Discovery and Data Mining: 9th Pacific-Asia Conference (PAKDD 2005)},
  publisher = {Springer Berlin Heidelberg},
  year = {2005},
  pages = {420--425},
  doi = {10.1007/11430919_49}
}
D-Stream
Chen Y and Tu L (2007), "Density-based Clustering for Real-time Stream Data", In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Jose, California, USA , pp. 133-142. ACM.
[BibTeX] [DOI]
@inproceedings{ChenTu2007,
  author = {Chen, Yixin and Tu, Li},
  title = {Density-based Clustering for Real-time Stream Data},
  booktitle = {Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  publisher = {ACM},
  year = {2007},
  pages = {133--142},
  doi = {10.1145/1281192.1281210}
}
D-Stream with attraction
Tu L and Chen Y (2009), "Stream Data Clustering Based on Grid Density and Attraction", ACM Trans. Knowl. Discov. Data. New York, NY, USA, 7, 2009. Vol. 3(3), pp. 12:1-12:27. ACM.
[BibTeX] [DOI]
@article{TuChen2009,
  author = {Tu, Li and Chen, Yixin},
  title = {Stream Data Clustering Based on Grid Density and Attraction},
  journal = {ACM Trans. Knowl. Discov. Data},
  publisher = {ACM},
  year = {2009},
  volume = {3},
  number = {3},
  pages = {12:1--12:27},
  doi = {10.1145/1552303.1552305}
}
DD-Stream
Jia C, Tan C and Yong A (2008), "A Grid and Density-Based Clustering Algorithm for Processing Data Stream", In Second International Conference on Genetic and Evolutionary Computing (WGEC '08)., 9, 2008. , pp. 517-521.
[BibTeX] [DOI]
@inproceedings{JiaTanYong2008,
  author = {C. Jia and C. Tan and A. Yong},
  title = {A Grid and Density-Based Clustering Algorithm for Processing Data Stream},
  booktitle = {Second International Conference on Genetic and Evolutionary Computing (WGEC '08)},
  year = {2008},
  pages = {517--521},
  doi = {10.1109/WGEC.2008.32}
}
ExCC
Bhatnagar V and Kaur S (2007), "Exclusive and Complete Clustering of Streams", In Database and Expert Systems Applications: Proceedings of the 18th International Conference (DEXA 2007). Berlin, Heidelberg , pp. 629-638. Springer Berlin Heidelberg.
[BibTeX] [DOI]
@inbook{BhatnagarKaur2007,
  author = {Bhatnagar, Vasudha and Kaur, Sharanjit},
  editor = {Wagner, Roland and Revell, Norman and Pernul, Günther},
  title = {Exclusive and Complete Clustering of Streams},
  booktitle = {Database and Expert Systems Applications: Proceedings of the 18th International Conference (DEXA 2007)},
  publisher = {Springer Berlin Heidelberg},
  year = {2007},
  pages = {629--638},
  doi = {10.1007/978-3-540-74469-6_61}
}
Bhatnagar V, Kaur S and Chakravarthy S (2014), "Clustering data streams using grid-based synopsis", Knowledge and Information Systems. Vol. 41(1), pp. 127-152.
[BibTeX] [DOI]
@article{BhatnagarKaurChakravarthy2014,
  author = {Bhatnagar, Vasudha and Kaur, Sharanjit and Chakravarthy, Sharma},
  title = {Clustering data streams using grid-based synopsis},
  journal = {Knowledge and Information Systems},
  year = {2014},
  volume = {41},
  number = {1},
  pages = {127--152},
  doi = {10.1007/s10115-013-0659-1}
}
DCUStream
Yang Y, Liu Z, p. Zhang J and Yang J (2012), "Dynamic density-based clustering algorithm over uncertain data streams", In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery., 5, 2012. , pp. 2664-2670.
[BibTeX] [DOI]
@inproceedings{YangLiuZhangEtAl2012,
  author = {Y. Yang and Z. Liu and J. p. Zhang and J. Yang},
  title = {Dynamic density-based clustering algorithm over uncertain data streams},
  booktitle = {2012 9th International Conference on Fuzzy Systems and Knowledge Discovery},
  year = {2012},
  pages = {2664-2670},
  doi = {10.1109/FSKD.2012.6233800}
}
DENGRIS-Stream
Amini A, Wah TY and Teh YW (2012), "DENGRIS-Stream: A Density-Grid based Clustering Algorithm for Evolving Data Streams over Sliding Window", In Proceedings of the International Conference on Data Mining and Computer Engineering., 12, 2012. , pp. 206-210.
[BibTeX]
@inproceedings{AminiWahTeh2012,
  author = {Amineh Amini and Teh Ying Wah and Ying Wah Teh},
  title = {DENGRIS-Stream: A Density-Grid based Clustering Algorithm for Evolving Data Streams over Sliding Window},
  booktitle = {Proceedings of the International Conference on Data Mining and Computer Engineering},
  year = {2012},
  pages = {206--210}
}
Fractal Clustering
Barbará D and Chen P (2003), "Using Self-Similarity to Cluster Large Data Sets", Data Mining and Knowledge Discovery. Vol. 7(2), pp. 123-152.
[BibTeX] [DOI]
@article{BarbaraChen2003,
  author = {Barbará, Daniel and Chen, Ping},
  title = {Using Self-Similarity to Cluster Large Data Sets},
  journal = {Data Mining and Knowledge Discovery},
  year = {2003},
  volume = {7},
  number = {2},
  pages = {123--152},
  doi = {10.1023/A:1022493416690}
}
Barbará D and Chen P (2000), "Using the Fractal Dimension to Cluster Datasets", In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Boston, Massachusetts, USA , pp. 260-264. ACM.
[BibTeX] [DOI]
@inproceedings{BarbaraChen2000,
  author = {Barbará, Daniel and Chen, Ping},
  title = {Using the Fractal Dimension to Cluster Datasets},
  booktitle = {Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  publisher = {ACM},
  year = {2000},
  pages = {260--264},
  doi = {10.1145/347090.347145}
}

Recursive Partitioning

Stats-Grid
Park NH and Lee WS (2004), "Statistical Grid-based Clustering over Data Streams", SIGMOD Rec.. New York, NY, USA, March, 2004. Vol. 33(1), pp. 32-37. ACM.
[BibTeX] [DOI]
@article{ParkLee2004,
  author = {Park, Nam Hun and Lee, Won Suk},
  title = {Statistical Grid-based Clustering over Data Streams},
  journal = {SIGMOD Rec.},
  publisher = {ACM},
  year = {2004},
  volume = {33},
  number = {1},
  pages = {32--37},
  doi = {10.1145/974121.974127}
}
Cell-Tree
Park NH and Lee WS (2007), "Cell trees: An adaptive synopsis structure for clustering multi-dimensional on-line data streams", Data & Knowledge Engineering . Vol. 63(2), pp. 528 - 549.
[BibTeX] [DOI]
@article{ParkLee2007,
  author = {Nam Hun Park and Won Suk Lee},
  title = {Cell trees: An adaptive synopsis structure for clustering multi-dimensional on-line data streams},
  journal = {Data & Knowledge Engineering },
  year = {2007},
  volume = {63},
  number = {2},
  pages = {528 - 549},
  doi = {10.1016/j.datak.2007.04.003}
}
MR-Stream
Wan L, Ng WK, Dang XH, Yu PS and Zhang K (2009), "Density-based Clustering of Data Streams at Multiple Resolutions", ACM Trans. Knowl. Discov. Data. New York, NY, USA, July, 2009. Vol. 3(3), pp. 14:1-14:28. ACM.
[BibTeX] [DOI]
@article{WanNgDangEtAl2009,
  author = {Wan, Li and Ng, Wee Keong and Dang, Xuan Hong and Yu, Philip S. and Zhang, Kuani},
  title = {Density-based Clustering of Data Streams at Multiple Resolutions},
  journal = {ACM Trans. Knowl. Discov. Data},
  publisher = {ACM},
  year = {2009},
  volume = {3},
  number = {3},
  pages = {14:1--14:28},
  doi = {10.1145/1552303.1552307}
}
PKSStream
Ren J, Cai B and Hu C (2011), "Clustering over Data Streams Based on Grid Density and Index Tree", Journal of Convergence Information Technology., 1, 2011. Vol. 6(1), pp. 83-93. AICIT.
[BibTeX] [DOI]
@article{RenCaiHu2011,
  author = {Jiadong Ren and Binlei Cai and Changzhen Hu},
  title = {Clustering over Data Streams Based on Grid Density and Index Tree},
  journal = {Journal of Convergence Information Technology},
  publisher = {AICIT},
  year = {2011},
  volume = {6},
  number = {1},
  pages = {83--93},
  doi = {10.4156/jcit.vol6.issue1.11}
}

Hybrid Grid-Approaches

HDCStream
Amini A, Saboohi H, Wah TY and Herawan T (2014), "A Fast Density-Based Clustering Algorithm for Real-Time Internet of Things Stream", The Scientific World Journal. Vol. 2014, pp. 1-11. Hindawi Publishing Corporation.
[BibTeX] [DOI]
@article{AminiSaboohiWahEtAl2014,
  author = {Amineh Amini and Hadi Saboohi and Teh Ying Wah and Tutut Herawan},
  title = {A Fast Density-Based Clustering Algorithm for Real-Time Internet of Things Stream},
  journal = {The Scientific World Journal},
  publisher = {Hindawi Publishing Corporation},
  year = {2014},
  volume = {2014},
  pages = {1--11},
  doi = {10.1155/2014/926020}
}
MuDi-Stream
Amini A, Saboohi H, Herawan T and Wah TY (2016), "MuDi-Stream: A multi density clustering algorithm for evolving data stream", Journal of Network and Computer Applications. Vol. 59, pp. 370 - 385.
[BibTeX] [DOI]
@article{AminiSaboohiHerawanEtAl2016,
  author = {Amineh Amini and Hadi Saboohi and Tutut Herawan and Teh Ying Wah},
  title = {MuDi-Stream: A multi density clustering algorithm for evolving data stream},
  journal = {Journal of Network and Computer Applications},
  year = {2016},
  volume = {59},
  pages = {370 -- 385},
  doi = {10.1016/j.jnca.2014.11.007}
}
MCDAStream
Shyam Sunder Reddy K and Shoba Bindu C (2018), "MCDAStream: a real-time data stream clustering based on micro-cluster density and attraction", International Journal of Engineering & Technology. Vol. 7(2), pp. 270-275.
[BibTeX] [DOI]
@article{ShyamSunderReddyShobaBindu2018,
  author = {Shyam Sunder Reddy K and Shoba Bindu C},
  title = {MCDAStream: a real-time data stream clustering based on micro-cluster density and attraction},
  journal = {International Journal of Engineering & Technology},
  year = {2018},
  volume = {7},
  number = {2},
  pages = {270--275},
  doi = {10.14419/ijet.v7i2.9051}
}

Model-Based Approaches

CluDistream
Zhou A, Cao F, Yan Y, Sha C and He X (2007), "Distributed Data Stream Clustering: A Fast EM-based Approach", In 2007 IEEE 23rd International Conference on Data Engineering., 4, 2007. , pp. 736-745.
[BibTeX] [DOI]
@inproceedings{ZhouCaoYanEtAl2007,
	author = {A. Zhou and F. Cao and Y. Yan and C. Sha and X. He},
	title = {Distributed Data Stream Clustering: A Fast EM-based Approach},
	booktitle = {2007 IEEE 23rd International Conference on Data Engineering},
	year = {2007},
	pages = {736--745},
	doi = {10.1109/ICDE.2007.367919}
}
SWEM
Dang XH, Lee VCS, Ng WK and Ong KL (2009), "Incremental and Adaptive Clustering Stream Data over Sliding Window", In Database and Expert Systems Applications: 20th International Conference (DEXA 2009). Berlin, Heidelberg , pp. 660-674. Springer Berlin Heidelberg.
[BibTeX] [DOI]
												@inbook{DangLeeNgEtAl2009a,
												  author = {Dang, Xuan Hong and Lee, Vincent C. S. and Ng, Wee Keong and Ong, Kok Leong},
												  editor = {Bhowmick, Sourav S. and Küng, Josef and Wagner, Roland},
												  title = {Incremental and Adaptive Clustering Stream Data over Sliding Window},
												  booktitle = {Database and Expert Systems Applications: 20th International Conference (DEXA 2009)},
												  publisher = {Springer Berlin Heidelberg},
												  year = {2009},
												  pages = {660--674},
												  doi = {10.1007/978-3-642-03573-9_55}
												}
												
Dang XH, Lee V, Ng WK, Ciptadi A and Ong KL (2009), "An EM-Based Algorithm for Clustering Data Streams in Sliding Windows", In Proceedings of the 14th International Conference on Database Systems for Advanced Applications (DASFAA 2009). Berlin, Heidelberg , pp. 230-235. Springer Berlin Heidelberg.
[BibTeX] [DOI]
@inproceedings{DangLeeNgEtAl2009,
  author = {Dang, Xuan Hong and Lee, Vincent and Ng, Wee Keong and Ciptadi, Arridhana and Ong, Kok Leong},
  editor = {Zhou, Xiaofang and Yokota, Haruo and Deng, Ke and Liu, Qing},
  title = {An EM-Based Algorithm for Clustering Data Streams in Sliding Windows},
  booktitle = {Proceedings of the 14th International Conference on Database Systems for Advanced Applications (DASFAA 2009)},
  publisher = {Springer Berlin Heidelberg},
  year = {2009},
  pages = {230--235},
  doi = {10.1007/978-3-642-00887-0_18}
}
COBWEB
Fisher DH (1987), "Knowledge acquisition via incremental conceptual clustering", Machine Learning. Vol. 2(2), pp. 139-172.
[BibTeX] [DOI]
@article{Fisher1987,
  author = {Fisher, Douglas H.},
  title = {Knowledge acquisition via incremental conceptual clustering},
  journal = {Machine Learning},
  year = {1987},
  volume = {2},
  number = {2},
  pages = {139--172},
  doi = {10.1007/BF00114265}
}
ICFR
Motoyoshi M, Miura T and Shioya I (2004), "Clustering Stream Data by Regression Analysis", In Proceedings of the Second Workshop on Australasian Information Security, Data Mining and Web Intelligence, and Software Internationalisation. Darlinghurst, Australia, Australia Vol. 32, pp. 115-120. Australian Computer Society, Inc..
[BibTeX]
@inproceedings{MotoyoshiMiuraShioya2004,
  author = {Motoyoshi, Masahiro and Miura, Takao and Shioya, Isamu},
  title = {Clustering Stream Data by Regression Analysis},
  booktitle = {Proceedings of the Second Workshop on Australasian Information Security, Data Mining and Web Intelligence, and Software Internationalisation},
  publisher = {Australian Computer Society, Inc.},
  year = {2004},
  volume = {32},
  pages = {115--120}
}
WStream
Tasoulis DK, Adams NM and Hand DJ (2006), "Unsupervised Clustering In Streaming Data", In Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)., 12, 2006. , pp. 638-642.
[BibTeX] [DOI]
@inproceedings{TasoulisAdamsHand2006,
  author = {D. K. Tasoulis and N. M. Adams and D. J. Hand},
  title = {Unsupervised Clustering In Streaming Data},
  booktitle = {Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)},
  year = {2006},
  pages = {638--642},
  doi = {10.1109/ICDMW.2006.165}
}
SVStream
Wang CD, Lai JH, Huang D and Zheng WS (2013), "SVStream: A Support Vector-Based Algorithm for Clustering Data Streams", IEEE Transactions on Knowledge and Data Engineering., 6, 2013. Vol. 25(6), pp. 1410-1424.
[BibTeX] [DOI]
@article{WangLaiHuangEtAl2013,
  author = {C. D. Wang and J. H. Lai and D. Huang and W. S. Zheng},
  title = {SVStream: A Support Vector-Based Algorithm for Clustering Data Streams},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2013},
  volume = {25},
  number = {6},
  pages = {1410--1424},
  doi = {10.1109/TKDE.2011.263}
}

Projected Approaches

HPStream
Aggarwal CC, Han J, Wang J and Yu PS (2004), "A Framework for Projected Clustering of High Dimensional Data Streams", In Proceedings of the Thirtieth International Conference on Very Large Data Bases. Toronto, Canada Vol. 30, pp. 852-863. VLDB Endowment.
[BibTeX]
@inproceedings{AggarwalHanWangEtAl2004,
  author = {Aggarwal, Charu C. and Han, Jiawei and Wang, Jianyong and Yu, Philip S.},
  title = {A Framework for Projected Clustering of High Dimensional Data Streams},
  booktitle = {Proceedings of the Thirtieth International Conference on Very Large Data Bases},
  publisher = {VLDB Endowment},
  year = {2004},
  volume = {30},
  pages = {852--863}
}
SiblingTree
Park NH and Lee WS (2007), "Grid-based Subspace Clustering over Data Streams", In Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management. New York, NY, USA , pp. 801-810. ACM.
[BibTeX] [DOI]
@inproceedings{ParkLee2007a,
  author = {Park, Nam Hun and Lee, Won Suk},
  title = {Grid-based Subspace Clustering over Data Streams},
  booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
  publisher = {ACM},
  year = {2007},
  pages = {801--810},
  doi = {10.1145/1321440.1321551}
}
HDDStream
Ntoutsi I, Zimek A, Palpanas T, Kröger P and Kriegel H-P (2012), "Density-based Projected Clustering over High Dimensional Data Streams", In Proceedings of the 2012 SIAM International Conference on Data Mining. , pp. 987-998.
[BibTeX] [DOI]
@inbook{NtoutsiZimekPalpanasEtAl2012,
  author = {Irene Ntoutsi and Arthur Zimek and Themis Palpanas and Peer Kröger and Hans-Peter Kriegel},
  title = {Density-based Projected Clustering over High Dimensional Data Streams},
  booktitle = {Proceedings of the 2012 SIAM International Conference on Data Mining},
  year = {2012},
  pages = {987--998},
  doi = {10.1137/1.9781611972825.85}
}
PreDeConStream
Hassani M, Spaus P, Gaber MM and Seidl T (2012), "Density-Based Projected Clustering of Data Streams", In Scalable Uncertainty Management: 6th International Conference, SUM 2012, Marburg, Germany, September 17-19, 2012. Proceedings. Berlin, Heidelberg , pp. 311-324. Springer Berlin Heidelberg.
[BibTeX] [DOI]
@inbook{HassaniSpausGaberEtAl2012,
  author = {Hassani, Marwan and Spaus, Pascal and Gaber, Mohamed Medhat and Seidl, Thomas},
  editor = {Hüllermeier, Eyke and Link, Sebastian and Fober, Thomas and Seeger, Bernhard},
  title = {Density-Based Projected Clustering of Data Streams},
  booktitle = {Scalable Uncertainty Management: 6th International Conference, SUM 2012, Marburg, Germany, September 17-19, 2012. Proceedings},
  publisher = {Springer Berlin Heidelberg},
  year = {2012},
  pages = {311--324},
  doi = {10.1007/978-3-642-33362-0_24}
}