Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/24787
Title: Fuzzy based integrated genetic algorithm and ant colony optimization scheme for uncertainty reduction in data mining tasks
Researcher: Sankar, K
Guide(s): Venkatachalam, V
Keywords: Ant Colony Optimization
Data mining
Genetic algorithm
Information and communication engineering
Upload Date: 9-Sep-2014
University: Anna University
Completed Date: 01/10/2013
Abstract: In the context of data mining many tasks are presented by the researchers together with classification regression clustering and dependence modeling Classification is one of the popular data mining tasks where the value of a discrete variable is predicted based on the values of available independent variables In this work the investigation was made to know how predictive classification models can be inferred from the available data The classification models are used to make good predictions based on the available data Data clustering techniques have significant role in analyzing classes of records that have wide range of applications where clustering algorithms are adopted in numerous fields of research The major task of clustering is to separate a set of data points into self similar data groups such that the points that belong to the same group will be much similar than the points the belongs to different groups Such a group is called a cluster Data are clustered using an iterative version of the Fuzzy C Means algorithm Ant Colony Optimization Scheme is a general purpose system which has been considered by the study of behavior of Ant Colonies It is based on co operative search paradigm that is applicable to the solution of combinatorial optimization problem Ant Colony Optimization is carried out to produce significantly simpler classification rule sets
Pagination: xix, 140p.
URI: http://hdl.handle.net/10603/24787
Appears in Departments:Faculty of Information and Communication Engineering

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02_certificate.pdf1.25 MBAdobe PDFView/Open
03_abstract.pdf220.04 kBAdobe PDFView/Open
04_acknowledgement.pdf58.67 kBAdobe PDFView/Open
05_contents.pdf668.75 kBAdobe PDFView/Open
06_chapter1.pdf164.72 kBAdobe PDFView/Open
07_chapter2.pdf113.86 kBAdobe PDFView/Open
08_chapter3.pdf254.39 kBAdobe PDFView/Open
09_chapter4.pdf391.85 kBAdobe PDFView/Open
10_chapter5.pdf258.79 kBAdobe PDFView/Open
11_chapter6.pdf199.49 kBAdobe PDFView/Open
12_chapter7.pdf19.69 kBAdobe PDFView/Open
13_references.pdf179.24 kBAdobe PDFView/Open
14_publications.pdf44.55 kBAdobe PDFView/Open
15_vitae.pdf19.06 kBAdobe PDFView/Open


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