Comparative analysis of K-Means and K-Medoids for clustering exam questions / Nurul Zafirah Mokhtar

Clustering has become more needed as a technique to cluster with intent to provide better grouping due to several problems. Clustering dynamic data is a challenge in identifying and forming groups. This unsupervised learning usually leads to undirected knowledge discovery. The cluster detection algo...

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Main Author:	Mokhtar, Nurul Zafirah
Format:	Thesis
Language:	English
Published:	2016
Subjects:	Analysis Algorithms
Online Access:	https://ir.uitm.edu.my/id/eprint/55833/1/55833.pdf
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id	my-uitm-ir.55833
record_format	uketd_dc
spelling	my-uitm-ir.558332024-05-24T07:41:15Z Comparative analysis of K-Means and K-Medoids for clustering exam questions / Nurul Zafirah Mokhtar 2016 Mokhtar, Nurul Zafirah Analysis Electronic Computers. Computer Science Algorithms Clustering has become more needed as a technique to cluster with intent to provide better grouping due to several problems. Clustering dynamic data is a challenge in identifying and forming groups. This unsupervised learning usually leads to undirected knowledge discovery. The cluster detection algorithm searches for clusters of data which are similar to one another by using similarity measures. Determining the suitable algorithm which can bring the optimized group clusters could be an issue. K-Means and k-Medoids are popular technique used in the world of clustering. Grouping an exam questions is a confusing tasks as it have to deal with the attributes and parameters of the data. Both techniques also may resulted in different outcomes. Depending on the parameters and attributes of the data, the results obtained from using both k-Means and k-Medoids could be varied. Each and every attribute and parameters selected undergo several process of data mining starting from pre-processing until the analysis of the data. The attributes and parameters that takes part in grouping the questions are marks, cognitive level and also the topics of the questions. Then the results is compared to determine which technique will produce higher accuracy results. This paper presents a comparative analysis of both algorithm in different data clusters to lay out strength and weaknesses of both. The grouping of an exam questions encompass low, medium and high level questions. Throughout the studies that conducted in determining the cluster, ITS570 course was used as a data and a set of cluster rules that hold the centroid and medoids value for both algorithm were produced at the end of this project for both techniques. The studies had found that k-Medoids produced higher accuracy result with 0.11% higher than k-Means. As a conclusion, with this type of data, k-Medoids algorithm had shown higher accuracy result rather than k-Means. 2016 Thesis https://ir.uitm.edu.my/id/eprint/55833/ https://ir.uitm.edu.my/id/eprint/55833/1/55833.pdf text en public degree Universiti Teknologi MARA Cawangan Melaka Faculty of Computer and Mathematical Sciences
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Analysis Analysis Algorithms
spellingShingle	Analysis Analysis Algorithms Mokhtar, Nurul Zafirah Comparative analysis of K-Means and K-Medoids for clustering exam questions / Nurul Zafirah Mokhtar
description	Clustering has become more needed as a technique to cluster with intent to provide better grouping due to several problems. Clustering dynamic data is a challenge in identifying and forming groups. This unsupervised learning usually leads to undirected knowledge discovery. The cluster detection algorithm searches for clusters of data which are similar to one another by using similarity measures. Determining the suitable algorithm which can bring the optimized group clusters could be an issue. K-Means and k-Medoids are popular technique used in the world of clustering. Grouping an exam questions is a confusing tasks as it have to deal with the attributes and parameters of the data. Both techniques also may resulted in different outcomes. Depending on the parameters and attributes of the data, the results obtained from using both k-Means and k-Medoids could be varied. Each and every attribute and parameters selected undergo several process of data mining starting from pre-processing until the analysis of the data. The attributes and parameters that takes part in grouping the questions are marks, cognitive level and also the topics of the questions. Then the results is compared to determine which technique will produce higher accuracy results. This paper presents a comparative analysis of both algorithm in different data clusters to lay out strength and weaknesses of both. The grouping of an exam questions encompass low, medium and high level questions. Throughout the studies that conducted in determining the cluster, ITS570 course was used as a data and a set of cluster rules that hold the centroid and medoids value for both algorithm were produced at the end of this project for both techniques. The studies had found that k-Medoids produced higher accuracy result with 0.11% higher than k-Means. As a conclusion, with this type of data, k-Medoids algorithm had shown higher accuracy result rather than k-Means.
format	Thesis
qualification_level	Bachelor degree
author	Mokhtar, Nurul Zafirah
author_facet	Mokhtar, Nurul Zafirah
author_sort	Mokhtar, Nurul Zafirah
title	Comparative analysis of K-Means and K-Medoids for clustering exam questions / Nurul Zafirah Mokhtar
title_short	Comparative analysis of K-Means and K-Medoids for clustering exam questions / Nurul Zafirah Mokhtar
title_full	Comparative analysis of K-Means and K-Medoids for clustering exam questions / Nurul Zafirah Mokhtar
title_fullStr	Comparative analysis of K-Means and K-Medoids for clustering exam questions / Nurul Zafirah Mokhtar
title_full_unstemmed	Comparative analysis of K-Means and K-Medoids for clustering exam questions / Nurul Zafirah Mokhtar
title_sort	comparative analysis of k-means and k-medoids for clustering exam questions / nurul zafirah mokhtar
granting_institution	Universiti Teknologi MARA Cawangan Melaka
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2016
url	https://ir.uitm.edu.my/id/eprint/55833/1/55833.pdf
_version_	1804889639265239040

Comparative analysis of K-Means and K-Medoids for clustering exam questions / Nurul Zafirah Mokhtar

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