Award Date

1-1-1997

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Committee Member

Kazem Taghva

Number of Pages

66

Abstract

Clustering and feedback have been used in information retrieval to improve the effectiveness of retrieving relevant documents. In this thesis, we investigate the retrieval effectiveness from various document collections in presence of both clustering and feedback. More precisely, we apply a clustering algorithm to an initial run of our queries to choose appropriate clusters for feedback. The clustering and feedback are done automatically.

Keywords

Automatic; Clustering; Feedback; Information; Retrieval

Controlled Subject

Computer science; Information science

File Format

pdf

File Size

1597.44 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Permissions

If you are the rightful copyright holder of this dissertation or thesis and wish to have the full text removed from Digital Scholarship@UNLV, please submit a request to digitalscholarship@unlv.edu and include clear identification of the work, preferably with URL.

Identifier

https://doi.org/10.25669/x8vc-ebtb


Share

COinS