The CLUSTER3 System For Goal-oriented
Conceptual Clustering: Method And Preliminary
Results
Author(s)
W. D. Seeman & R.S.Michalski
Abstract
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.
Keywords: conceptual clustering, unsupervised learning, goal-oriented clustering,
pattern recognition, attributional calculus, view-relevant attribute subsetting.
1 Introduction
Clustering analysis is a fundamental way of gleaning knowledge from data when
little is known about the organization of the observations recorded in the data.
Most clustering methods group objects into clusters solely on the basis of the
relationship of the observations to each other, and output results as collections or
hierarchies of clustered observations. They typically take descriptions of objects
in the form of attribute-value vectors and group the descriptions on the basis of
Keywords
conceptual clustering, unsupervised learning, goal-oriented clustering,pattern recognition, attributional calculus, view-relevant attribute subsetting.
Related Book
Other papers in this volume
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