Improved k-means clustering using principal component analysis and imputation methods for breast cancer dataset

Data mining techniques have been used to analyse pattern from data sets in order to derive useful information. Classification of data sets into clusters is one of the essential process for data manipulation. One of the most popular and efficient clustering methods is K-means method. However, the K-m...

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Main Author:	Armina, Roslan
Format:	Thesis
Language:	English
Published:	2018
Subjects:	QA76 Computer software
Online Access:	http://eprints.utm.my/id/eprint/81435/1/RoslanArminaMFC2018.pdf
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id	my-utm-ep.81435
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spelling	my-utm-ep.814352019-08-23T05:01:06Z Improved k-means clustering using principal component analysis and imputation methods for breast cancer dataset 2018 Armina, Roslan QA76 Computer software Data mining techniques have been used to analyse pattern from data sets in order to derive useful information. Classification of data sets into clusters is one of the essential process for data manipulation. One of the most popular and efficient clustering methods is K-means method. However, the K-means clustering method has some difficulties in the analysis of high dimension data sets with the presence of missing values. Moreover, previous studies showed that high dimensionality of the feature in data set presented poses different problems for K-means clustering. For missing value problem, imputation method is needed to minimise the effect of incomplete high dimensional data sets in K-means clustering process. This research studies the effect of imputation algorithm and dimensionality reduction techniques on the performance of K-means clustering. Three imputation methods are implemented for the missing value estimation which are K-nearest neighbours (KNN), Least Local Square (LLS), and Bayesian Principle Component Analysis (BPCA). Principal Component Analysis (PCA) is a dimension reduction method that has a dimensional reduction capability by removing the unnecessary attribute of high dimensional data sets. Hence, PCA hybrid with K-means (PCA K-means) is proposed to give a better clustering result. The experimental process was performed by using Wisconsin Breast Cancer. By using LLS imputation method, the proposed hybrid PCA K-means outperformed the standard Kmeans clustering based on the results for breast cancer data set; in terms of clustering accuracy (0.29%) and computing time (95.76%). 2018 Thesis http://eprints.utm.my/id/eprint/81435/ http://eprints.utm.my/id/eprint/81435/1/RoslanArminaMFC2018.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:119420 masters Universiti Teknologi Malaysia Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Armina, Roslan Improved k-means clustering using principal component analysis and imputation methods for breast cancer dataset
description	Data mining techniques have been used to analyse pattern from data sets in order to derive useful information. Classification of data sets into clusters is one of the essential process for data manipulation. One of the most popular and efficient clustering methods is K-means method. However, the K-means clustering method has some difficulties in the analysis of high dimension data sets with the presence of missing values. Moreover, previous studies showed that high dimensionality of the feature in data set presented poses different problems for K-means clustering. For missing value problem, imputation method is needed to minimise the effect of incomplete high dimensional data sets in K-means clustering process. This research studies the effect of imputation algorithm and dimensionality reduction techniques on the performance of K-means clustering. Three imputation methods are implemented for the missing value estimation which are K-nearest neighbours (KNN), Least Local Square (LLS), and Bayesian Principle Component Analysis (BPCA). Principal Component Analysis (PCA) is a dimension reduction method that has a dimensional reduction capability by removing the unnecessary attribute of high dimensional data sets. Hence, PCA hybrid with K-means (PCA K-means) is proposed to give a better clustering result. The experimental process was performed by using Wisconsin Breast Cancer. By using LLS imputation method, the proposed hybrid PCA K-means outperformed the standard Kmeans clustering based on the results for breast cancer data set; in terms of clustering accuracy (0.29%) and computing time (95.76%).
format	Thesis
qualification_level	Master's degree
author	Armina, Roslan
author_facet	Armina, Roslan
author_sort	Armina, Roslan
title	Improved k-means clustering using principal component analysis and imputation methods for breast cancer dataset
title_short	Improved k-means clustering using principal component analysis and imputation methods for breast cancer dataset
title_full	Improved k-means clustering using principal component analysis and imputation methods for breast cancer dataset
title_fullStr	Improved k-means clustering using principal component analysis and imputation methods for breast cancer dataset
title_full_unstemmed	Improved k-means clustering using principal component analysis and imputation methods for breast cancer dataset
title_sort	improved k-means clustering using principal component analysis and imputation methods for breast cancer dataset
granting_institution	Universiti Teknologi Malaysia
granting_department	Computing
publishDate	2018
url	http://eprints.utm.my/id/eprint/81435/1/RoslanArminaMFC2018.pdf
_version_	1747818331075772416

Improved k-means clustering using principal component analysis and imputation methods for breast cancer dataset

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