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Temporal mining of Web and supermark...

Yan, Rui.

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Temporal mining of Web and supermarket data using fuzzy and rough set clustering.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Temporal mining of Web and supermarket data using fuzzy and rough set clustering./
Author:	Yan, Rui.
Description:	117 p.
Notes:	Source: Masters Abstracts International, Volume: 43-03, page: 0895.
Contained By:	Masters Abstracts International43-03.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ96136
ISBN:	0612961362

Temporal mining of Web and supermarket data using fuzzy and rough set clustering.
Yan, Rui.

Temporal mining of Web and supermarket data using fuzzy and rough set clustering. - 117 p.

Source: Masters Abstracts International, Volume: 43-03, page: 0895.

Thesis (M.Sc.)--Saint Mary's University (Canada), 2004.

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.

ISBN: 0612961362Subjects--Topical Terms:

626642
Computer Science.

Temporal mining of Web and supermarket data using fuzzy and rough set clustering.
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100 1 $a Yan, Rui. $3 1929566
245 1 0 $a Temporal mining of Web and supermarket data using fuzzy and rough set clustering.
300 $a 117 p.
500 $a Source: Masters Abstracts International, Volume: 43-03, page: 0895.
500 $a Adviser: Pawan Lingras.
502 $a Thesis (M.Sc.)--Saint Mary's University (Canada), 2004.
520 $a 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.
590 $a School code: 1104.
650 4 $a Computer Science. $3 626642
650 4 $a Statistics. $3 517247
650 4 $a Artificial Intelligence. $3 769149
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690 $a 0463
690 $a 0800
710 2 0 $a Saint Mary's University (Canada). $3 1020608
773 0 $t Masters Abstracts International $g 43-03.
790 1 0 $a Lingras, Pawan, $e advisor
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791 $a M.Sc.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ96136