Algorithms for clustering data
Algorithms for clustering data
Pattern recognition and image analysis
Pattern recognition and image analysis
ACM Computing Surveys (CSUR)
Partitioning-based clustering for Web document categorization
Decision Support Systems - Special issue on WITS '97
Document Categorization and Query Generation on the World Wide WebUsing WebACE
Artificial Intelligence Review - Special issue on data mining on the Internet
Principal Direction Divisive Partitioning
Data Mining and Knowledge Discovery
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis
Applied Soft Computing
K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, we focus on the problem of unsupervised clustering of a data-set. We introduce the traditional K-Means (K-means) cluster analysis and fuzzy C-means (FCM) cluster analysis of the principles and algorithms process at first, then a novel method to initialize the cluster centers is proposed. The idea is that the cluster centers' distribution should be as evenly as possible within the input field. A "Two-step method" is used in our evolutionary models, with evolutionary algorithms to get the initialized centers, and traditional methods to get the final results. Experiment results show our initialization method can speed up the convergence, and in some cases, make the algorithm performs better.