Sequential pattern recognition procedures derived from multiple Fourier series
Pattern Recognition Letters
Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Temporal and spatio-temporal aggregations over data streams using multiple time granularities
Information Systems - Special issue: Best papers from EDBT 2002
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering
ICML '01 Proceedings of the Eighteenth International Conference on 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
Optimal Histograms with Quality Guarantees
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Issues in data stream management
ACM SIGMOD Record
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Statistical grid-based clustering over data streams
ACM SIGMOD Record
ST-DBSCAN: An algorithm for clustering spatial-temporal data
Data & Knowledge Engineering
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A Weighted Fuzzy Clustering Algorithm for Data Stream
CCCM '08 Proceedings of the 2008 ISECS International Colloquium on Computing, Communication, Control, and Management - Volume 01
Computational Intelligence: Methods and Techniques
Computational Intelligence: Methods and Techniques
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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Clustering is a one of the most important tasks of data mining. Algorithms like the Fuzzy C-Means and Possibilistic C-Means provide good result both for the static data and data streams. All clustering algorithms compute centers from chunk of data, what requires a lot of time. If the rate of incoming data is faster than speed of algorithm, part of data will be lost. To prevent such situation, some pre-processing algorithms should be used. The purpose of this paper is to propose a pre-processing method for clustering algorithms. Experimental results show that proposed method is appropriate to handle noisy data and can accelerate processing time.