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Article: A semi-supervised regression model for mixed numerical and categorical variables

TitleA semi-supervised regression model for mixed numerical and categorical variables
Authors
KeywordsCategorical variables
Clustering
Data mining
Numerical variables
Regression
Issue Date2007
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pr
Citation
Pattern Recognition, 2007, v. 40 n. 6, p. 1745-1752 How to Cite?
AbstractIn this paper, we develop a semi-supervised regression algorithm to analyze data sets which contain both categorical and numerical attributes. This algorithm partitions the data sets into several clusters and at the same time fits a multivariate regression model to each cluster. This framework allows one to incorporate both multivariate regression models for numerical variables (supervised learning methods) and k-mode clustering algorithms for categorical variables (unsupervised learning methods). The estimates of regression models and k-mode parameters can be obtained simultaneously by minimizing a function which is the weighted sum of the least-square errors in the multivariate regression models and the dissimilarity measures among the categorical variables. Both synthetic and real data sets are presented to demonstrate the effectiveness of the proposed method. © 2006 Pattern Recognition Society.
Persistent Identifierhttp://hdl.handle.net/10722/75264
ISSN
2015 Impact Factor: 3.399
2015 SCImago Journal Rankings: 2.051
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorNg, MKen_HK
dc.contributor.authorChan, EYen_HK
dc.contributor.authorSo, MMCen_HK
dc.contributor.authorChing, WKen_HK
dc.date.accessioned2010-09-06T07:09:29Z-
dc.date.available2010-09-06T07:09:29Z-
dc.date.issued2007en_HK
dc.identifier.citationPattern Recognition, 2007, v. 40 n. 6, p. 1745-1752en_HK
dc.identifier.issn0031-3203en_HK
dc.identifier.urihttp://hdl.handle.net/10722/75264-
dc.description.abstractIn this paper, we develop a semi-supervised regression algorithm to analyze data sets which contain both categorical and numerical attributes. This algorithm partitions the data sets into several clusters and at the same time fits a multivariate regression model to each cluster. This framework allows one to incorporate both multivariate regression models for numerical variables (supervised learning methods) and k-mode clustering algorithms for categorical variables (unsupervised learning methods). The estimates of regression models and k-mode parameters can be obtained simultaneously by minimizing a function which is the weighted sum of the least-square errors in the multivariate regression models and the dissimilarity measures among the categorical variables. Both synthetic and real data sets are presented to demonstrate the effectiveness of the proposed method. © 2006 Pattern Recognition Society.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pren_HK
dc.relation.ispartofPattern Recognitionen_HK
dc.subjectCategorical variablesen_HK
dc.subjectClusteringen_HK
dc.subjectData miningen_HK
dc.subjectNumerical variablesen_HK
dc.subjectRegressionen_HK
dc.titleA semi-supervised regression model for mixed numerical and categorical variablesen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0031-3203&volume=40&spage=1745&epage=1752&date=2007&atitle=A+Semi-Supervised+Regression+Model+for+Mixed+Numerical+and+Categorical+Variablesen_HK
dc.identifier.emailChing, WK:wching@hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.patcog.2006.06.018en_HK
dc.identifier.scopuseid_2-s2.0-33947104960en_HK
dc.identifier.hkuros126429en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33947104960&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume40en_HK
dc.identifier.issue6en_HK
dc.identifier.spage1745en_HK
dc.identifier.epage1752en_HK
dc.identifier.isiWOS:000245745000010-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridNg, MK=34571761900en_HK
dc.identifier.scopusauthoridChan, EY=16038954500en_HK
dc.identifier.scopusauthoridSo, MMC=16040240300en_HK
dc.identifier.scopusauthoridChing, WK=13310265500en_HK

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