Metric Incremental Clustering of Nominal Data
Dan A. Simovici, Natima Singla, Michael Kuperberg · 2005
We present an algorithm/or 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.