Artificial Immune System Based Data Miner

The goal of this project is to show how to solve data mining classification problems using Clonal Selection Algorithm (CLONALG). A comparisons has been proposed between the CLONALG and other classification techniques to show the performance and the accuracy of it.

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Bibliographic Details
Main Author: Hassan Babiker, Amir Samir
Format: Thesis
Published: 2011
Subjects:
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id my-mmu-ep.3348
record_format uketd_dc
spelling my-mmu-ep.33482012-01-18T07:17:52Z Artificial Immune System Based Data Miner 2011-01 Hassan Babiker, Amir Samir QA76.75-76.765 Computer software The goal of this project is to show how to solve data mining classification problems using Clonal Selection Algorithm (CLONALG). A comparisons has been proposed between the CLONALG and other classification techniques to show the performance and the accuracy of it. 2011-01 Thesis http://shdl.mmu.edu.my/3348/ http://vlib.mmu.edu.my/diglib/login/dlusr/login.php masters University of Multimedia Research Library
institution Multimedia University
collection MMU Institutional Repository
topic QA76.75-76.765 Computer software
spellingShingle QA76.75-76.765 Computer software
Hassan Babiker, Amir Samir
Artificial Immune System Based Data Miner
description The goal of this project is to show how to solve data mining classification problems using Clonal Selection Algorithm (CLONALG). A comparisons has been proposed between the CLONALG and other classification techniques to show the performance and the accuracy of it.
format Thesis
qualification_level Master's degree
author Hassan Babiker, Amir Samir
author_facet Hassan Babiker, Amir Samir
author_sort Hassan Babiker, Amir Samir
title Artificial Immune System Based Data Miner
title_short Artificial Immune System Based Data Miner
title_full Artificial Immune System Based Data Miner
title_fullStr Artificial Immune System Based Data Miner
title_full_unstemmed Artificial Immune System Based Data Miner
title_sort artificial immune system based data miner
granting_institution University of Multimedia
granting_department Research Library
publishDate 2011
_version_ 1747829482077552640