Metric Incremental Clustering of Nominal Data

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Fourth IEEE International Conference on Data Mining (ICDM'04)

Brighton, United Kingdom

November 01-November 04

ISBN: 0-7695-2142-8

	ASCII Text	x
	Dan Simovici, Namita Singla, Michael Kuperberg, "Metric Incremental Clustering of Nominal Data," Data Mining, IEEE International Conference on, pp. 523-526, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004.

	BibTex	x
	@article{ 10.1109/ICDM.2004.10005, author = {Dan Simovici and Namita Singla and Michael Kuperberg}, title = {Metric Incremental Clustering of Nominal Data}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2004}, isbn = {0-7695-2142-8}, pages = {523-526}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10005}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Metric Incremental Clustering of Nominal Data SN - 0-7695-2142-8 SP523 EP526 A1 - Dan Simovici, A1 - Namita Singla, A1 - Michael Kuperberg, PY - 2004 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Dan Simovici, University of Massachusetts at Boston

Namita Singla, University of Massachusetts at Boston

Michael Kuperberg, Karlsruhe University, Germany

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10005

We present an algorithm for clustering nominal data that is based on a metric on the set of partitions of a finite set of objects; this metric is defined starting from a lower valuation of the lattice of partitions. The proposed algorithm seeks to determine a clustering partition such that the total distance between this partition and the partitions determined by the attributes of the objects has a local minimum. The resulting clustering is quite stable relative to the ordering of the objects.

Citation:

Dan Simovici, Namita Singla, Michael Kuperberg, "Metric Incremental Clustering of Nominal Data," icdm, pp.523-526, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004

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