Dissertation/Thesis Abstract

Mining discriminating patterns in data with confidence
by Kamra, Varun, M.S., California State University, Long Beach, 2016, 59; 10196147
Abstract (Summary)

There are many pattern mining algorithms available for classifying data. The main drawback of most of the algorithms is that they always focus on mining frequent patterns in data that may not always be discriminative enough for classification. There could exist patterns that are not frequent, but are efficient discriminators. In such cases these algorithms might not perform well. This project proposes the MDP algorithm, which aims to search for patterns that are good at discriminating between classes rather than searching for frequent patterns. The MDP ensures that there is at least one most discriminative pattern (MDP) per record. The purpose of the project is to investigate how a structural approach to classification compares to a functional approach. The project has been developed in Java programming language.

Indexing (document details)
Advisor: Ebert, Todd
Commitee: Englert, Burkhard, Johnson, Thomas G.
School: California State University, Long Beach
Department: Computer Engineering and Computer Science
School Location: United States -- California
Source: MAI 56/02M(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Computer science
Keywords: Data mining
Publication Number: 10196147
ISBN: 9781369320398
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