Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
ACODF: a novel data clustering approach for data mining in large databases
Journal of Systems and Software - Special issue: Performance modeling and analysis of computer systems and networks
ANGEL: a new effective and efficient hybrid clustering technique for large databases
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
G-TREACLE: a new grid-based and tree-alike pattern clustering technique for large databases
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Numerous existing partitioning clustering algorithms, such as K-means, are developed to discover clusters that fit some of the static models. These algorithms may fail if it chooses a set of incorrect parameters in the static model with respect to the objects being clustered, or when the objects consist of patterns that are of non-spherical or not the same size. Furthermore, they could produce an instable result. This investigation presents a new partition clustering algorithm named SDCC, which can improve the problem of instable results in partitioning-based clustering, such as K-means. As a hybrid approach that utilizes double-centroid concept, the proposed algorithm can eliminate the above-mentioned drawbacks to produce stable results while recognizing the non-spherical patterns and clusters that are not the same size. Experimental results illustrate that the new algorithm can identify non-spherical pattern correctly, and efficiently reduces the problem of long computational time when applying KGA and GKA. It also indicates that the proposed approach produces much smaller errors than K-means, KGA and GKA approaches in most cases examined herein.