The CLUSTER3 system for goal-oriented conceptual clustering: method and preliminary results

摘要

A conceptual clustering program CLUSTER3 is described that, given a set of objects represented by attribute-value tuples, groups them into clusters described by generalized conjunctive descriptions in attributional calculus. The descriptions are optimized according to a user-designed multi-criterion clustering quality measure. The clustering process in CLUSTER3 depends on a viewpoint underlying the clustering goal, and employs the view-relevant attribute subsetting method (VAS) that selects for clustering only attributes relevant to this viewpoint. The program is illustrated by a simple designed problem and by its application to clustering of US Congressional voting records. The ongoing research concerns application of CLUSTER3 to large and complex datasets such as collections of web pages.