Temporal mining of the web and supermarket data using fuzzy and rough set clustering

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dc.contributor.advisor Lingras, Pawan
dc.creator Yan, Rui
dc.date.accessioned 2011-05-09T12:32:26Z
dc.date.available 2011-05-09T12:32:26Z
dc.date.issued 2004
dc.identifier.other QA278 Y36 2004
dc.identifier.uri http://library2.smu.ca/xmlui/handle/01/22622
dc.description xviii, 117 leaves : ill. (some col.) ; 28 cm.
dc.description Includes abstract.
dc.description Includes bibliographical references (leaves 114-117).
dc.description.abstract Clustering is an important aspect of data mining. Many data mining applications tend to be more amenable to non-conventional clustering techniques. In this research three clustering methods are employed to analyze the web usage and super market data sets: conventional, rough set and fuzzy methods. Interval clusters based on fuzzy memberships are also created. The web usage data were collected from three educational web sites. The supermarket data spanned twenty-six weeks of transactions from twelve stores spanning three regions. Cluster sizes obtained using the three methods are compared, and cluster characteristics are analyzed. Web users and supermarket customers tend to change their characteristics over a period of time. These changes may be temporary or permanent. This thesis also studies the changes in cluster characteristics over time. Both experiments demonstrate that the rough and fuzzy methods are more subtle and accurate in capturing the slight differences among clusters.
dc.description.provenance Made available in DSpace on 2011-05-09T12:32:26Z (GMT). No. of bitstreams: 0 en
dc.language.iso en
dc.publisher Halifax, N.S. : Saint Mary's University
dc.subject.lcc QA278
dc.subject.lcsh Data mining
dc.subject.lcsh Cluster analysis
dc.subject.lcsh Web usage mining
dc.subject.lcsh Internet users
dc.subject.lcsh Consumer behavior
dc.title Temporal mining of the web and supermarket data using fuzzy and rough set clustering
dc.type Text
thesis.degree.name Master of Science in Applied Science
thesis.degree.level Masters
thesis.degree.discipline Mathematics and Computing Science
thesis.degree.grantor Saint Mary's University (Halifax, N.S.)
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