Sequential pattern recognition procedures derived from multiple Fourier series
Pattern Recognition Letters
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Modeling for Control
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Connectionist Structures of Type 2 Fuzzy Inference Systems
PPAM '01 Proceedings of the th International Conference on Parallel Processing and Applied Mathematics-Revised Papers
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Computational Intelligence: Methods and Techniques
Computational Intelligence: Methods and Techniques
Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Proceedings of the 2010 conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications
Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications
Neuro-fuzzy systems with relation matrix
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Boosting ensemble of relational neuro-fuzzy systems
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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In the paper the clustering algorithms based on fuzzy set theory are considered. Modifications of the Fuzzy C-Means and the Possibilistic C-Means algorithms are presented, which adjust them to deal with data streams. Since data stream is of infinite size, it has to be partitioned into chunks. Simulations show that this partitioning procedure does not affect the quality of clustering results significantly. Moreover, properly chosen weights can be assigned to each data element. This modification allows the presented algorithms to handle concept drift during simulations.