A generalization based hybrid algorithm for clustering semi-structured data

Author

Ming-Yi Shih

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 analysis
Algorithms -- Computer programs
Data mining

Thesis Number

T 8556

Print OCLC #

62211969

Link to Catalog Record

Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.

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