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Estimation of Evolutionary Optimization Algorithm for Association Rule using Spatial Data Mining

by N. Naga Saranya, M. Hemalatha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 51 - Number 3
Year of Publication: 2012
Authors: N. Naga Saranya, M. Hemalatha
10.5120/8019-8204

N. Naga Saranya, M. Hemalatha . Estimation of Evolutionary Optimization Algorithm for Association Rule using Spatial Data Mining. International Journal of Computer Applications. 51, 3 ( August 2012), 1-5. DOI=10.5120/8019-8204

@article{ 10.5120/8019-8204,
author = { N. Naga Saranya, M. Hemalatha },
title = { Estimation of Evolutionary Optimization Algorithm for Association Rule using Spatial Data Mining },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 51 },
number = { 3 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume51/number3/8019-8204/ },
doi = { 10.5120/8019-8204 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:49:25.762332+05:30
%A N. Naga Saranya
%A M. Hemalatha
%T Estimation of Evolutionary Optimization Algorithm for Association Rule using Spatial Data Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 51
%N 3
%P 1-5
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The innovative process for spatial data is more risk when compared to relational data. This can be functional for the efficiency and effectiveness of algorithms as well as the difficulty of possible patterns that can be establish in a spatial database. To optimize the rules generated by Association Rule Mining (Apriori method) [1] use hybrid evolutionary algorithm. This research paper present a novel hybrid evolutionary algorithm (HEA) [2] which uses particle swarm optimization for spatial association rule mining with clustering. The proposed HEA algorithm is to enhance the performance of Multi objective genetic algorithm [3][4] by incorporating local search, particle swarm optimization (PSO), for Multi objective association rule mining. Thereafter, particle swarm is performed to come out of local optima. From the experiment results, it is shown that the proposed HEA algorithm has superior performance when compared to other existing algorithms.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Spatial Data Mining Apriori Algorithm Satellite Data Hybrid Evolutionary Algorithm Particle Swarm Optimization

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