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Content Provider | IEEE Xplore Digital Library |
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Author | Saha, S. Bandyopadhyay, S. |
Copyright Year | 2007 |
Description | Author affiliation: Indian Stat. Inst., Kolkata (Saha, S.; Bandyopadhyay, S.) |
Abstract | Identification of the correct number of clusters and the corresponding partitioning are two important considerations in clustering. In this paper, a newly developed point symmetry based distance is used to propose symmetry based versions of six cluster validity indices namely, DB-index, Dunn-index, generalized Dunn-index, PS-index, I-index and XB-index. These indices provide measures of "symmetricity" of the different partitionings of a data set. A Kd-tree-based data structure is used to reduce the complexity of computing the symmetry distance. A newly developed genetic point symmetry based clustering technique, GAPS-clustering is used as the underlying partitioning algorithm. The number of clusters are varied from 2 to radicn where n is the total number of data points present in the data set and the values of all the validity indices are noted down. The optimum value of a validity index over these radicn-1 partitions corresponds to the appropriate partitioning and the number of partitions as indicated by the validity index. Results on five artificially generated and four real-life data sets show that symmetry distance based I-index performs the best compared to all the other five indices. |
Starting Page | 697 |
Ending Page | 704 |
File Size | 498641 |
Page Count | 8 |
File Format | |
ISBN | 9781424413393 |
DOI | 10.1109/CEC.2007.4424539 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2007-09-25 |
Publisher Place | Singapore |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Clustering algorithms Partitioning algorithms Euclidean distance Machine intelligence Data structures Particle measurements Shape Genetic algorithms Virtual manufacturing Clustering methods |
Content Type | Text |
Resource Type | Article |
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