Symbolic clustering using a new similarity measure

A hierarchical, agglomerative, symbolic clustering methodology based on a similarity measure that takes into consideration the position, span, and content of symbolic objects is proposed and is capable of discerning clusters in data sets made up of numeric as well as symbolic objects consisting of different types and combinations of qualitative and quantitative feature values.

Abstract

A hierarchical, agglomerative, symbolic clustering methodology based on a similarity measure that takes into consideration the position, span, and content of symbolic objects is proposed. The similarity measure used is of a new type in the sense that it is not just another aspect of dissimilarity. The clustering methodology forms composite symbolic objects using a Cartesian join operator when two symbolic objects are merged. The maximum and minimum similarity values at various merging levels permit the determination of the number of clusters in the data set. The composite symbolic objects representing different clusters give a description of the resulting classes and lead to knowledge acquisition. The algorithm is capable of discerning clusters in data sets made up of numeric as well as symbolic objects consisting of different types and combinations of qualitative and quantitative feature values. In particular, the algorithm is applied to fat-oil and microcomputer data.>