Investigation of machine learning tools for document clustering and classification

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2000

Author dc:creator

• Borodavkina, Lyudmila, 1977-

Advisor dc:contributor.advisor

• David R. Karger.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/8932

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/8932