A fuzzy data mining system

Dissertant

Maelainin, Sidi Ali

Thesis advisor

Bin Said, Amini

University

Al Akhawayn University

Faculty

School of Science and Engineering

Department

Computer Science

University Country

Morocco

Degree

Master

Degree Date

1999

English Abstract

Data Mining has emerged as one of the hottest topics in the field of information storage and retrieval, and is increasingly understood to be a critical business issue throughout the corporate world.

New technologies have taken the business world by storm, and the enthusiasm for Data Mining reveals a shrewd awareness among decision makers across many industry sectors for the potential of a technology that can transform business performance Nevertheless, as is the case for many new technologies, the benefits and applications of Data Mining remain poorly understood by many users and potential users.

In order to address the main issues and promote a broader awareness of Data Mining, this thesis first introduces the reader to the field of Data Mining, and then suggests a new way of introducing fuzzy logic in it.

The introduction of fuzzy logic in Data Mining allows for the representation and manipulation of imprecise or uncertain data and vague queries, which extends the range of applications of future Data Mining systems.

In this thesis, we suggest a potential enhancement of the traditional (crisp) Data Mining systems to build Fuzzy Data Mining Systems (FDMSs).

The proposed systems make it possible to take advantage of the mining tools used by conventional (non-fuzzy) systems simply by introducing fuzzy logic on top of these tools rather than by building fuzzy Data Mining tools.

In this research, we propose a model for a fuzzy Data Mining system (FDMS), a fuzzy Data Mining query language (FDMQL), as well as a fuzzy Data Mining tool built on top of a crisp classifier.

Main Subjects

Information Technology and Computer Science

Topics

No. of Pages

Table of contents.

Abstract.

Abstract in Arabic.3.

Chapter One : Introduction.

Chapter Two : Data mining.

Chapter Three : Data mining functions and techniques.

Chapter Four : A model for a fuzzy data mining system.

Chapter Five : Fuzzy data mining query language.

Chapter Six : FDMQL for generation of fuzzy rules using a crisp rule.

Chapter Seven : Conclusions and directions for future research.

References.

American Psychological Association (APA)

Maelainin, Sidi Ali. (1999). A fuzzy data mining system. (Master's theses Theses and Dissertations Master). Al Akhawayn University, Morocco
https://search.emarefa.net/detail/BIM-646690

Modern Language Association (MLA)

Maelainin, Sidi Ali. A fuzzy data mining system. (Master's theses Theses and Dissertations Master). Al Akhawayn University. (1999).
https://search.emarefa.net/detail/BIM-646690

American Medical Association (AMA)