A generalization based hybrid algorithm for clustering semi-structured data
"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.
Ph. D. in Computer Science
University of Missouri--Rolla
xiii, 137 pages
© 2004 Ming-Yi Shih, All rights reserved.
Dissertation - Citation
Algorithms -- Computer programs
Print OCLC #
Link to Catalog Record
Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.http://merlin.lib.umsystem.edu/record=b5381783~S5
Shih, Ming-Yi, "A generalization based hybrid algorithm for clustering semi-structured data" (2004). Doctoral Dissertations. 1590.
Share My Dissertation If you are the author of this work and would like to grant permission to make it openly accessible to all, please click the button above.