2026-09-23T14:02:31Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/89322022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
David R. Karger.
Borodavkina, Lyudmila, 1977-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-08-23T16:27:55Z
2005-08-23T16:27:55Z
2000
2000
http://hdl.handle.net/1721.1/8932
48981692
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (leaves 57-59).
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.
by Lyudmila Borodavkina.
M.Eng.
59 leaves
5253610 bytes
5253368 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Investigation of machine learning tools for document clustering and classification
Application of machine learning algorithms for document clustering and classification
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="a7cbc6ba-a5d4-4a16-9814-cc8826538e04">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Investigation of machine learning tools for document clustering and classification</Title>
<Subtitle>Application of machine learning algorithms for document clustering and classification</Subtitle>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2000</PublicationDate>
<Authors>
<Author>
<DisplayName>Borodavkina, Lyudmila, 1977-</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<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.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>