Sequential pattern recognition procedures derived from multiple Fourier series
Pattern Recognition Letters
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Connectionist Structures of Type 2 Fuzzy Inference Systems
PPAM '01 Proceedings of the th International Conference on Parallel Processing and Applied Mathematics-Revised Papers
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Load Shedding for Aggregation Queries over Data Streams
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
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
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
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 this paper the resource consumption of the fuzzy clustering algorithms for data streams is studied. As the examples, the wFCM and the wPCM algorithms are examined. It is shown that partitioning a data stream into chunks reduces the processing time of considered algorithms significantly. The partitioning procedure is accompanied with the reduction of results accuracy, however the change is acceptable. The problems arised due to the high speed data streams are presented as well. The uncontrolable growth of subsequent data chunk sizes, which leads to the overflow of the available memory, is demonstrated for both the wFCM and wPCM algorithms. The maximum chunk size limit modification, as a solution to this problem, is introduced. This modification ensures that the available memory is never exceeded, what is shown in the simulations. The considered modification decreases the quality of clustering results only slightly.