Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Self-organizing maps
ACM Computing Surveys (CSUR)
Cure: an efficient clustering algorithm for large databases
Information Systems
Machine Learning
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Proceedings of the 2008 ACM symposium on Applied computing
A comparison of extrinsic clustering evaluation metrics based on formal constraints
Information Retrieval
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
A Fast and Stable Incremental Clustering Algorithm
ITNG '10 Proceedings of the 2010 Seventh International Conference on Information Technology: New Generations
λ-Perceptron: An adaptive classifier for data streams
Pattern Recognition
Classification and novel class detection of data streams in a dynamic feature space
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
An agent-based approach to care in independent living
AmI'10 Proceedings of the First international joint conference on Ambient intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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Several research fields have described phenomena that produce endless sequences of samples, referred to as data streams. These phenomena are studied using data clustering models continuously obtained throughout the endless data gathering process, whose set of dynamical properties, i.e., behavior, evolves over time. In order to cope with data streams characteristics, researchers have developed clustering techniques with low time-complexity requirements. However, pre-defined and static parameters thresholds, number of clusters and learning rates commonly found in current techniques still limit the application of clustering to data streams. These limitations to adapt clustering process to behavior changes motivated this paper to propose an on-line and adaptive approach to detect changes and modify parameters. The proposed approach is based on the traditional k-means algorithm to update cluster prototypes and the statistical model of Markov chains to represent behavior. Behavior changes are detected by testing the isomorphism of Markov chains over time under the grounds of Dynamical Systems Theory. The results have confirmed the advantages of the approach when compared with current techniques.