Doctoral Dissertations
A generalization based hybrid algorithm for clustering semi-structured data
Abstract
"In this work, a generalized based methodology that combines attribute hierarchy construction, object generalization and data clustering is presented. The algorithm works well on semi-structured data and requires only a minimum of domain knowledge. Since the algorithm reduces the dimensionality of the semi-structured data, clustering of the resulting generalized data often requires less execution time and computer memory"--Abstract, page iii.
Department(s)
Computer Science
Degree Name
Ph. D. in Computer Science
Publisher
University of Missouri--Rolla
Publication Date
Summer 2004
Pagination
xiii, 137 pages
Note about bibliography
Includes bibliographical references (pages 130-136).
Rights
© 2004 Ming-Yi Shih, All rights reserved.
Document Type
Dissertation - Citation
File Type
text
Language
English
Subject Headings
Cluster analysisAlgorithms -- Computer programsData mining
Thesis Number
T 8556
Print OCLC #
62211969
Recommended Citation
Shih, Ming-Yi, "A generalization based hybrid algorithm for clustering semi-structured data" (2004). Doctoral Dissertations. 1590.
https://scholarsmine.mst.edu/doctoral_dissertations/1590
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