Path: utzoo!utgpu!news-server.csri.toronto.edu!rpi!bu.edu!bucasb!reynolds From: reynolds@park.bu.edu (John Reynolds) Newsgroups: comp.ai.neural-nets Subject: Re: Clustering Message-ID: Date: 22 May 91 19:12:38 GMT References: <1991May20.203008.27681@noose.ecn.purdue.edu> Sender: news@bu.edu Organization: Boston University Center for Adaptive Systems Lines: 29 In-reply-to: greenba@gambia.crd.ge.com's message of 21 May 91 14:10:44 GMT >>>>> On 21 May 91 14:10:44 GMT, greenba@gambia.crd.ge.com (ben a green) said: ben> Clustering is a way to sort things into groups that share similarities. ben> If you already know the classes to which the things belong, what's the ben> point of trying to cluster them? In addition to code compression, which is a consequence of both supervised and unsupervised clustering, some supervised clustering algorithms can allow generalization. Some systems can tessellate the input space into homogeneous regions containing only patterns of a single class. If such regions can be identified, then an informed guess can be made about the class membership of future, unlabeled patterns, and they can be treated accordingly. Moreover, it is often inappropriate to group patterns according to ostensible similarity because the values of important variables may not be known. Two objects may appear similar but they may differ in unknown but important variables. Some supervised clustering algorithms can locally warp the similarity metric so that functionally similar patterns are grouped together and vice versa. Dimensions which are useful in separating functionally different classes of objects are enhanced, and irrelevant dimensions are compressed. John Reynolds