Clustering chemical data set using particle swarm optimization based algorithm

Clustering is the process of organizing similar objects into groups, with its main objective is to organize a collection of data items into some meaningful groups. Generally, clustering is the most suitable approach in dealing with huge amount dataset with higher resemblance such as chemical databas...

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Main Author: Triyono, Triyono
Format: Thesis
Language:English
Published: 2008
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Online Access:http://eprints.utm.my/id/eprint/9867/1/TriyonoMFKM2008.pdf
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spelling my-utm-ep.98672018-10-14T07:21:28Z Clustering chemical data set using particle swarm optimization based algorithm 2008-04 Triyono, Triyono QA75 Electronic computers. Computer science Clustering is the process of organizing similar objects into groups, with its main objective is to organize a collection of data items into some meaningful groups. Generally, clustering is the most suitable approach in dealing with huge amount dataset with higher resemblance such as chemical database. The chemical data sets contain a huge number of compounds and knowledge of the physiochemical properties. The biological activities of these compounds have a large significance in the process of designing and discovering new drugs. Many algorithms had been applied to cluster chemical data set such as Ward’s algorithm. In this study, Particle Swarm Optimization (PSO) based clustering algorithm is exploited to optimize the results of other clustering algorithm such as K-means. Two chemical data sets were used and downloaded from MDDR (MDL Drug Database Report). The main difference between these two data sets is measured in terms of the similarities quantify of bioactivities between active compounds. The results are compared with Ward’s algorithm in terms of proportion actives percentage in active clusters are. We found that PSO algorithm reveals better performance than Ward’s algorithm on continuous data format; however for binary data format, Ward’s algorithm outperforms arrogantly. 2008-04 Thesis http://eprints.utm.my/id/eprint/9867/ http://eprints.utm.my/id/eprint/9867/1/TriyonoMFKM2008.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:1277 masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution Universiti Teknologi Malaysia
collection UTM Institutional Repository
language English
topic QA75 Electronic computers
Computer science
spellingShingle QA75 Electronic computers
Computer science
Triyono, Triyono
Clustering chemical data set using particle swarm optimization based algorithm
description Clustering is the process of organizing similar objects into groups, with its main objective is to organize a collection of data items into some meaningful groups. Generally, clustering is the most suitable approach in dealing with huge amount dataset with higher resemblance such as chemical database. The chemical data sets contain a huge number of compounds and knowledge of the physiochemical properties. The biological activities of these compounds have a large significance in the process of designing and discovering new drugs. Many algorithms had been applied to cluster chemical data set such as Ward’s algorithm. In this study, Particle Swarm Optimization (PSO) based clustering algorithm is exploited to optimize the results of other clustering algorithm such as K-means. Two chemical data sets were used and downloaded from MDDR (MDL Drug Database Report). The main difference between these two data sets is measured in terms of the similarities quantify of bioactivities between active compounds. The results are compared with Ward’s algorithm in terms of proportion actives percentage in active clusters are. We found that PSO algorithm reveals better performance than Ward’s algorithm on continuous data format; however for binary data format, Ward’s algorithm outperforms arrogantly.
format Thesis
qualification_level Master's degree
author Triyono, Triyono
author_facet Triyono, Triyono
author_sort Triyono, Triyono
title Clustering chemical data set using particle swarm optimization based algorithm
title_short Clustering chemical data set using particle swarm optimization based algorithm
title_full Clustering chemical data set using particle swarm optimization based algorithm
title_fullStr Clustering chemical data set using particle swarm optimization based algorithm
title_full_unstemmed Clustering chemical data set using particle swarm optimization based algorithm
title_sort clustering chemical data set using particle swarm optimization based algorithm
granting_institution Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department Faculty of Computer Science and Information System
publishDate 2008
url http://eprints.utm.my/id/eprint/9867/1/TriyonoMFKM2008.pdf
_version_ 1747814783757844480