by T C Havens, J C Bezdek, J M Keller, M Popescu

Abstract:

This paper presents a new technique for clustering either object or relational data. First, the data are represented as a matrix D of dissimilarity values. D is reordered to D∗ using a visual assessment of cluster tendency algorithm. If the data contain clusters, they are suggested by visually apparent dark squares arrayed along the main diagonal of an image I (D∗ ) of D∗ . The suggested clusters in the object set underlying the reordered relational data are found by defining an objective function that recognizes this blocky structure in the reordered data. The objective function is optimized when the boundaries in I (D∗ ) are matched by those in an aligned partition of the objects. The objective function combines measures of contrast and edginess and is optimized by particle swarm optimization. We prove that the set of aligned partitions is exponentially smaller than the set of partitions that needs to be searched if clusters are sought in D. Six numerical examples are given to illustrate various facets of the algorithm.

Reference:

Clustering in ordered dissimilarity data (T C Havens, J C Bezdek, J M Keller, M Popescu), In International Journal of Intelligent Systems, Wiley Online Library, volume 24, 2009.

Bibtex Entry:

@article{Havens2009, abstract = {This paper presents a new technique for clustering either object or relational data. First, the data are represented as a matrix D of dissimilarity values. D is reordered to D∗ using a visual assessment of cluster tendency algorithm. If the data contain clusters, they are suggested by visually apparent dark squares arrayed along the main diagonal of an image I (D∗ ) of D∗ . The suggested clusters in the object set underlying the reordered relational data are found by defining an objective function that recognizes this blocky structure in the reordered data. The objective function is optimized when the boundaries in I (D∗ ) are matched by those in an aligned partition of the objects. The objective function combines measures of contrast and edginess and is optimized by particle swarm optimization. We prove that the set of aligned partitions is exponentially smaller than the set of partitions that needs to be searched if clusters are sought in D. Six numerical examples are given to illustrate various facets of the algorithm.}, author = {Havens, T C and Bezdek, J C and Keller, J M and Popescu, M}, doi = {10.1002/int}, journal = {International Journal of Intelligent Systems}, keywords = {SML-LIB-BIBLIO,lang:ENG}, mendeley-tags = {SML-LIB-BIBLIO,lang:ENG}, number = {5}, pages = {504--528}, publisher = {Wiley Online Library}, title = {{Clustering in ordered dissimilarity data}}, url = {http://onlinelibrary.wiley.com/doi/10.1002/int.20344/abstract}, volume = {24}, year = {2009} }

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