Designing cross-validation consensus clustering with reference point in determining the optimal number of clusters

Consensus clustering has an ability to overcome instability in estimating the number of clusters, k faced by traditional clustering approach. Consensus clustering offers better estimate by consolidating clustering results into an optimal value. However, the consensus clustering approach faced with t...

Full description

Saved in:

Bibliographic Details
Main Author:	Norin Rahayu, Shamsuddin
Format:	Thesis
Language:	eng eng eng
Published:	2022
Subjects:	QA Mathematics
Online Access:	https://etd.uum.edu.my/9744/1/permission%20to%20deposit-900985.pdf https://etd.uum.edu.my/9744/2/s900985_01.pdf https://etd.uum.edu.my/9744/3/s900985_02.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uum-etd.9744
record_format	uketd_dc
spelling	my-uum-etd.97442022-08-14T01:25:58Z Designing cross-validation consensus clustering with reference point in determining the optimal number of clusters 2022 Norin Rahayu, Shamsuddin Mahat, Nor Idayu Che Dom, Nazri Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Art & Sciences QA Mathematics Consensus clustering has an ability to overcome instability in estimating the number of clusters, k faced by traditional clustering approach. Consensus clustering offers better estimate by consolidating clustering results into an optimal value. However, the consensus clustering approach faced with three weakness which are lack of clear rules for construction of multiple base partitions, B; lack of specific procedure in combining the outcome of clustering from B into a single consolidated value; and suffers from excessive computational time and complexity in identifying k. Motivated by those weaknesses, this study designs a cross-validation consensus clustering using reference point at every base partition to obtain optimal number of clusters, ˘ky to produce more robust and stable results. The proposed design creates base partitions using a 10-fold cross-validation approach. In each base partition, the reference point was imposed by extracting 30% of the objects from a dataset to identify ˘ky. The ˘ky is used to cluster the objects and identify its clusters. The designed was tested on both simulated and real datasets using stability index, heatmap visualisation and clustering validations. The findings showed that the proposed design performs better in term of computational times in clustering the objects in less than one minute once ˘ky is obtained. The results also revealed that clustering throughout base partitions in both simulated and real datasets are robust and stable. The proposed design works well on non-overlapping clusters or unequal size of objects cases with least completion time for clustering process. The design also competitive to other clustering approaches in high overlapping clusters and unclear structure of clusters problems. 2022 Thesis https://etd.uum.edu.my/9744/ https://etd.uum.edu.my/9744/1/permission%20to%20deposit-900985.pdf text eng 2023-03-06 staffonly https://etd.uum.edu.my/9744/2/s900985_01.pdf text eng 2023-03-06 staffonly https://etd.uum.edu.my/9744/3/s900985_02.pdf text eng 2023-03-06 staffonly other doctoral Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng eng
advisor	Mahat, Nor Idayu Che Dom, Nazri
topic	QA Mathematics
spellingShingle	QA Mathematics Norin Rahayu, Shamsuddin Designing cross-validation consensus clustering with reference point in determining the optimal number of clusters
description	Consensus clustering has an ability to overcome instability in estimating the number of clusters, k faced by traditional clustering approach. Consensus clustering offers better estimate by consolidating clustering results into an optimal value. However, the consensus clustering approach faced with three weakness which are lack of clear rules for construction of multiple base partitions, B; lack of specific procedure in combining the outcome of clustering from B into a single consolidated value; and suffers from excessive computational time and complexity in identifying k. Motivated by those weaknesses, this study designs a cross-validation consensus clustering using reference point at every base partition to obtain optimal number of clusters, ˘ky to produce more robust and stable results. The proposed design creates base partitions using a 10-fold cross-validation approach. In each base partition, the reference point was imposed by extracting 30% of the objects from a dataset to identify ˘ky. The ˘ky is used to cluster the objects and identify its clusters. The designed was tested on both simulated and real datasets using stability index, heatmap visualisation and clustering validations. The findings showed that the proposed design performs better in term of computational times in clustering the objects in less than one minute once ˘ky is obtained. The results also revealed that clustering throughout base partitions in both simulated and real datasets are robust and stable. The proposed design works well on non-overlapping clusters or unequal size of objects cases with least completion time for clustering process. The design also competitive to other clustering approaches in high overlapping clusters and unclear structure of clusters problems.
format	Thesis
qualification_name	other
qualification_level	Doctorate
author	Norin Rahayu, Shamsuddin
author_facet	Norin Rahayu, Shamsuddin
author_sort	Norin Rahayu, Shamsuddin
title	Designing cross-validation consensus clustering with reference point in determining the optimal number of clusters
title_short	Designing cross-validation consensus clustering with reference point in determining the optimal number of clusters
title_full	Designing cross-validation consensus clustering with reference point in determining the optimal number of clusters
title_fullStr	Designing cross-validation consensus clustering with reference point in determining the optimal number of clusters
title_full_unstemmed	Designing cross-validation consensus clustering with reference point in determining the optimal number of clusters
title_sort	designing cross-validation consensus clustering with reference point in determining the optimal number of clusters
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2022
url	https://etd.uum.edu.my/9744/1/permission%20to%20deposit-900985.pdf https://etd.uum.edu.my/9744/2/s900985_01.pdf https://etd.uum.edu.my/9744/3/s900985_02.pdf
_version_	1747828663075733504

Designing cross-validation consensus clustering with reference point in determining the optimal number of clusters

Similar Items