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Conference Paper: An interactive approach to building classification models by clustering and cluster validation

TitleAn interactive approach to building classification models by clustering and cluster validation
Authors
Issue Date2000
PublisherSpringer-Verlag.
Citation
The 2nd International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents, Hong Kong, 13-15 December 2000, p. 23-28 How to Cite?
AbstractThis paper presents the decision clusters classifier (DCC) for database mining. A DCC model consists of a small set of decision clusters extracted from a tree of clusters generated by a clustering algorithm from the training data set. A decision cluster is associated to one of the classes in the data set and used to determine the class of new objects. A DCC model classifies new objects by deciding which decision clusters these objects belong to. In making classification decisions, DCC is similar to the k-nearest neighbor classification scheme but its model building process is different. In this paper, we describe an interactive approach to building DCC models by stepwise clustering the training data set and validating the clusters using data visualization techniques. Our initial results on some public benchmarking data sets have shown that DCC models outperform the some existing popular classification methods.
Persistent Identifierhttp://hdl.handle.net/10722/93240
ISBN

 

DC FieldValueLanguage
dc.contributor.authorHuang, Zen_HK
dc.contributor.authorNg, Men_HK
dc.contributor.authorLin, Ten_HK
dc.contributor.authorCheung, DWL-
dc.date.accessioned2010-09-25T14:55:07Z-
dc.date.available2010-09-25T14:55:07Z-
dc.date.issued2000en_HK
dc.identifier.citationThe 2nd International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents, Hong Kong, 13-15 December 2000, p. 23-28-
dc.identifier.isbn3-540-41450-9-
dc.identifier.urihttp://hdl.handle.net/10722/93240-
dc.description.abstractThis paper presents the decision clusters classifier (DCC) for database mining. A DCC model consists of a small set of decision clusters extracted from a tree of clusters generated by a clustering algorithm from the training data set. A decision cluster is associated to one of the classes in the data set and used to determine the class of new objects. A DCC model classifies new objects by deciding which decision clusters these objects belong to. In making classification decisions, DCC is similar to the k-nearest neighbor classification scheme but its model building process is different. In this paper, we describe an interactive approach to building DCC models by stepwise clustering the training data set and validating the clusters using data visualization techniques. Our initial results on some public benchmarking data sets have shown that DCC models outperform the some existing popular classification methods.-
dc.languageengen_HK
dc.publisherSpringer-Verlag.-
dc.relation.ispartofIDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agentsen_HK
dc.titleAn interactive approach to building classification models by clustering and cluster validationen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailCheung, DWL: dcheung@cs.hku.hken_HK
dc.identifier.authorityCheung, DWL=rp00101en_HK
dc.identifier.hkuros58040en_HK

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