Investigation of machine learning tools for document clustering and classification
Name
48981692-MIT.pdf
Description
Full printable version
Size
2.2 MB
Format
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Checksum (MD5)
6eccf801f75faf37df08913ff5916a3b
Author(s)
Borodavkina, Lyudmila, 1977-
Advisor(s)
David R. Karger.
Alternative Title
Application of machine learning algorithms for document clustering and classification
Date Issued
2000
Publisher
Massachusetts Institute of Technology
Abstract
Data clustering is a problem of discovering the underlying data structure without any prior information about the data. The focus of this thesis is to evaluate a few of the modern clustering algorithms in order to determine their performance in adverse conditions. Synthetic Data Generation software is presented as a useful tool both for generating test data and for investigating results of the data clustering. Several theoretical models and their behavior are discussed, and, as the result of analysis of a large number of quantitative tests, we come up with a set of heuristics that describe the quality of clustering output in different adverse conditions.
Description
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (leaves 57-59).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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