BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Using the fractal dimension to cluster datasets
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Tri-plots: scalable tools for multidimensional data mining
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Tracking Clusters in Evolving Data Sets
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
COOLCAT: an entropy-based algorithm for categorical clustering
Proceedings of the eleventh international conference on Information and knowledge management
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Incremental and effective data summarization for dynamic hierarchical clustering
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Using retrieval measures to assess similarity in mining dynamic web clickstreams
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Online Hierarchical Clustering in a Data Warehouse Environment
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Computer Networks: The International Journal of Computer and Telecommunications Networking - Web dynamics
Density-based clustering for real-time stream data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Spider algorithm for clustering time series
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
E-Stream: Evolution-Based Technique for Stream Clustering
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
A Dynamic Clustering Algorithm for Mobile Objects
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
A system for analysis and prediction of electricity-load streams
Intelligent Data Analysis - Knowledge Discovery from Data Streams
Stream data clustering based on grid density and attraction
ACM Transactions on Knowledge Discovery from Data (TKDD)
Learning from Data Streams: Synopsis and Change Detection
Proceedings of the 2008 conference on STAIRS 2008: Proceedings of the Fourth Starting AI Researchers' Symposium
C-DenStream: Using Domain Knowledge on a Data Stream
DS '09 Proceedings of the 12th International Conference on Discovery Science
Resource aware distributed knowledge discovery
Ubiquitous knowledge discovery
L2GClust: local-to-global clustering of stream sources
Proceedings of the 2011 ACM Symposium on Applied Computing
Resource aware distributed knowledge discovery
Ubiquitous knowledge discovery
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
A grid-based clustering algorithm for high-dimensional data streams
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Monitoring incremental histogram distribution for change detection in data streams
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
Stream-dashboard: a framework for mining, tracking and validating clusters in a data stream
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
A single pass algorithm for clustering evolving data streams based on swarm intelligence
Data Mining and Knowledge Discovery
Data stream clustering: A survey
ACM Computing Surveys (CSUR)
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Scientific and industrial examples of data streams abound in astronomy, telecommunication operations, banking and stock-market applications, e-commerce and other fields. A challenge imposed by continuously arriving data streams is to analyze them and to modify the models that explain them as new data arrives. In this paper, we analyze the requirements needed for clustering data streams. We review some of the latest algorithms in the literature and assess if they meet these requirements.