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Fuzzy reasoning based evolutionary a...

Chen, Min.

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Fuzzy reasoning based evolutionary algorithms applied to data mining.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fuzzy reasoning based evolutionary algorithms applied to data mining./
Author:	Chen, Min.
Description:	122 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.
Contained By:	Dissertation Abstracts International76-12B(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3718074
ISBN:	9781321977929

Fuzzy reasoning based evolutionary algorithms applied to data mining.
Chen, Min.

Fuzzy reasoning based evolutionary algorithms applied to data mining. - 122 p.

Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.

Thesis (Ph.D.)--North Dakota State University, 2015.

Data mining and information retrieval are two difficult tasks for various reasons. First, as the volume of data increases tremendously, most of the data are complex, large, imprecise, uncertain or incomplete. Furthermore, information retrieval may be imprecise or subjective. Therefore, comprehensible and understandable results are required by the users during the process of data mining or knowledge discovery. Fuzzy logic has become an active research area because its capability of handling perceptual uncertainties, such as ambiguity or vagueness, and its excellent ability on describing nonlinear system.

ISBN: 9781321977929Subjects--Topical Terms:

523869
Computer science.

Fuzzy reasoning based evolutionary algorithms applied to data mining.
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100 1 $a Chen, Min. $3 704385
245 1 0 $a Fuzzy reasoning based evolutionary algorithms applied to data mining.
300 $a 122 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.
500 $a Adviser: Simone A. Ludwig.
502 $a Thesis (Ph.D.)--North Dakota State University, 2015.
520 $a Data mining and information retrieval are two difficult tasks for various reasons. First, as the volume of data increases tremendously, most of the data are complex, large, imprecise, uncertain or incomplete. Furthermore, information retrieval may be imprecise or subjective. Therefore, comprehensible and understandable results are required by the users during the process of data mining or knowledge discovery. Fuzzy logic has become an active research area because its capability of handling perceptual uncertainties, such as ambiguity or vagueness, and its excellent ability on describing nonlinear system.
520 $a The study of this dissertation is focused on two main paradigms. The first paradigm focuses on applying fuzzy inductive learning on classification problems. A fuzzy classifier based on discrete particle swarm optimization and a fuzzy decision tree classifier are implemented in this paradigm. The fuzzy classifier based on discrete particle swarm optimization includes a discrete particle swarm optimization classifier and a fuzzy discrete particle swarm optimization classifier. The discrete particle swarm optimization classifier is devised and applied to discrete data. Whereas, the fuzzy discrete particle swarm optimization classifier is an improved version that can handle both discrete and continuous data to manage uncertainty and imprecision. A fuzzy decision tree classifier with a feature selection method is proposed, which is based on the ideas of mutual information and genetic algorithms.
520 $a The second paradigm is fuzzy cluster analysis. The purpose is to provide efficient approaches to identify similar or dissimilar descriptions of data instances. The shapes of the clusters is either hyper-spherical or hyper-planed. A fuzzy c-means clustering approach based on particle swarm optimization, which clustering prototype is hyper-spherical, is proposed to automatically determine the optimal number of clusters. In addition, a fuzzy c-regression model, which has hyper-planed clusters, has received much attention in recent literature for nonlinear system identification and has been successfully employed in various areas. Thus, a fuzzy c-regression model clustering algorithm is applied for color image segmentation.
590 $a School code: 0157.
650 4 $a Computer science. $3 523869
690 $a 0984
710 2 $a North Dakota State University. $b Computer Science. $3 2102468
773 0 $t Dissertation Abstracts International $g 76-12B(E).
790 $a 0157
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3718074