Award Date

1-1-2006

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Committee Member

Kazem Taghva

Number of Pages

63

Abstract

One of the most commonly used data mining techniques is document clustering or unsupervised document classification which deals with the grouping of documents based on some document similarity function; This thesis deals with research issues associated with categorizing documents using the k-means clustering algorithm which groups objects into K number of groups based on document representations and similarities; The proposed hypothesis of this thesis is to prove that unsupervised clustering of a set of documents produces similar results to that of their supervised categorization.

Keywords

Algorithms; Clustering; Document Means; Study

Controlled Subject

Computer science

File Format

pdf

File Size

1484.8 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Permissions

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Identifier

https://doi.org/10.25669/22cq-7e2w


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