<?xml version="1.0" encoding="UTF-8"?>
<article article-type="research-article" xml:lang="en" xmlns:xlink="http://www.w3.org/1999/xlink">
<front>
<journal-meta>
<journal-id journal-id-type="publisher">global-journal-of-computer-science-and-technology</journal-id>
<journal-title-group>
<journal-title>Global Journal of Computer Science and Technology</journal-title>
</journal-title-group>
<issn publication-format="print">0975-4350</issn>
<issn publication-format="electronic">0975-4172</issn>
<publisher><publisher-name>Global Journals Publishing Group Incorporated</publisher-name></publisher>
<self-uri xlink:href="https://globaljournals.org/journal-seo-export/jats/73103.xml" />
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">73103</article-id>
<title-group>
<article-title>A Two step optimized spatial Association rule Mining Algorithm by hybrid evolutionary algorithm and cluster segmentation.</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author"><name><surname>J.Arunadevi</surname><given-names>Dr.</given-names></name><xref ref-type="aff" rid="aff1" />
</contrib>
</contrib-group>
<aff id="aff1">INDIA, Thiagarajar School of Management</aff>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2011-05-25">
<day>25</day>
<month>05</month>
<year>2011</year>
</pub-date>
<volume>11</volume>
<issue>12</issue>
<fpage>1</fpage>
<lpage>7</lpage>
<abstract><p>A novel two step approach by adopting hybrid evolutionary algorithm with cluster segmentation for Spatial Association Rule mining (SAR) is presented in this paper.Here first step concentrates on the optimization of SAR using the hybrid evolutionary algorithm which uses genetic algorithm and ant colony optimization (ACO). Multi objective genetic algorithm is used to provide the diversity of associations. ACO is performed to come out of local optima. In the second step, cluster the generated association rules used for the target group segmentation. Preferential based segmentation of the women of various groups belongs to the Madurai city, Tamilnadu, India. Here, number of rules generated by the first step of our SAR is minimized, also time generation for the rules are also minimized. Lift ratio increased for the generated rules.</p></abstract>
<kwd-group kwd-group-type="author-generated">
<kwd>SAR</kwd>
<kwd>MOGA</kwd>
<kwd>ACO</kwd>
<kwd>clustering</kwd>
<kwd>segmentation.</kwd>
</kwd-group>
<self-uri content-type="pdf" xlink:href="https://globaljournals.org/GJCST_Volume11/1-A-Two-Step-optimized-Spatial.pdf" />
<self-uri content-type="html" xlink:href="https://globaljournals.org/scholarly-articles/a-two-step-optimized-spatial-association-rule-mining-algorithm-by-hybrid-evolutionary-algorithm-and-cluster-segmentation/" />
</article-meta>
</front>
<body>
<sec>
<title>Full Text</title>
<p>A novel two step approach by adopting hybrid evolutionary algorithm with cluster segmentation for Spatial Association Rule mining (SAR) is presented in this paper.Here first step concentrates on the optimization of SAR using the hybrid evolutionary algorithm which uses genetic algorithm and ant colony optimization (ACO). Multi objective genetic algorithm is used to provide the diversity of associations. ACO is performed to come out of local optima. In the second step, cluster the generated association rules used for the target group segmentation. Preferential based segmentation of the women of various groups belongs to the Madurai city, Tamilnadu, India. Here, number of rules generated by the first step of our SAR is minimized, also time generation for the rules are also minimized. Lift ratio increased for the generated rules.</p>
</sec>
</body>
</article>