Masters Theses

Abstract

"Web mining can be broadly defined as the discovery and analysis of useful information from the World Wide Web. This broad definition bifurcates into two branches, the first, describes the automatic search and retrieval of information and resources available from millions of sites and on-line databases, i.e., Web content mining. The second branch explains the discovery and analysis of user access patterns from Web usage data, i.e., Web usage mining. User Clustering analysis allows one to group together clients or data items that have similar characteristics.

In this thesis, a method for the clustering of the Web usage sessions based on access patterns is proposed. Access patterns of the Web users are extracted from Web servers' log files, and then organized into sessions which represent episodes of interaction between Web users and the Web server. Using Attributed-Oriented-Induction, the sessions are then generalized according to a page hierarchy that organizes pages according to their generalities. The generalized sessions are finally clustered using existing hierarchical clustering method, BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) enhanced with Beam Search. Our experiments on a large real data set show that the method is efficient and practical for Web mining applications"--Abstract, page iii.

Advisor(s)

Fu, Yongjian

Committee Member(s)

Dekock, Arlan R.
Kluczny, Raymond Michael

Department(s)

Computer Science

Degree Name

M.S. in Computer Science

Comments

University of Missouri Research Board (Grant R-3-42434)

Publisher

University of Missouri--Rolla

Publication Date

Spring 2000

Pagination

viii, 42 pages

Note about bibliography

Includes bibliographical references (pages 40-41).

Rights

© 2000 Kanwalpreet Singh Sandhu, All rights reserved.

Document Type

Thesis - Restricted Access

File Type

text

Language

English

Thesis Number

T 7771

Print OCLC #

44651361

Electronic OCLC #

1106486748

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