Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster

K-Means is one of the unsupervised learning and partitioning clustering algorithms. It is very popular and widely used for its simplicity and fastness. The main drawback of this algorithm is that user should specify the number of cluster in advance. As an iterative clustering strategy, K-Means algor...

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Main Authors:	Wan Maseri, Wan Mohd, Beg, Abul Hashem, Tutut, Herawan, K., F.Rabbi
Format:	Article
Language:	English
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/6871/ http://umpir.ump.edu.my/id/eprint/6871/ http://umpir.ump.edu.my/id/eprint/6871/1/Max-D_clustering_K-means_algorithm_for_Autogeneration.pdf

id	ump-6871
recordtype	eprints
spelling	ump-68712017-09-14T03:43:40Z http://umpir.ump.edu.my/id/eprint/6871/ Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster Wan Maseri, Wan Mohd Beg, Abul Hashem Tutut, Herawan K., F.Rabbi QA76 Computer software K-Means is one of the unsupervised learning and partitioning clustering algorithms. It is very popular and widely used for its simplicity and fastness. The main drawback of this algorithm is that user should specify the number of cluster in advance. As an iterative clustering strategy, K-Means algorithm is very sensitive to the initial starting conditions. In this paper has been proposed a clustering technique called MaxD K-Means clustering algorithm. MaxD K-Means algorithm auto generates initial k (the desired number of cluster) without asking for input from the user. MaxD k-means also used a novel strategy of setting the initial centroids. The experiment of the Max-D means has been conducted using synthetic data, which is taken from the Llyod’s K-Means experiments. Another experiment has been done using reallife data focusing on student’s results in higher-education institution in Malaysia. The results from the new algorithm show that the number of iteration improves tremendously, and the number of iterations is reduced. The improvement rate is around 78%. Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/6871/1/Max-D_clustering_K-means_algorithm_for_Autogeneration.pdf Wan Maseri, Wan Mohd and Beg, Abul Hashem and Tutut, Herawan and K., F.Rabbi Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster. Fundamental Research Grant Scheme. pp. 15-21. http://onlinepresent.org/proceedings/vol7_2012/3.pdf
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Wan Maseri, Wan Mohd Beg, Abul Hashem Tutut, Herawan K., F.Rabbi Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster
description	K-Means is one of the unsupervised learning and partitioning clustering algorithms. It is very popular and widely used for its simplicity and fastness. The main drawback of this algorithm is that user should specify the number of cluster in advance. As an iterative clustering strategy, K-Means algorithm is very sensitive to the initial starting conditions. In this paper has been proposed a clustering technique called MaxD K-Means clustering algorithm. MaxD K-Means algorithm auto generates initial k (the desired number of cluster) without asking for input from the user. MaxD k-means also used a novel strategy of setting the initial centroids. The experiment of the Max-D means has been conducted using synthetic data, which is taken from the Llyod’s K-Means experiments. Another experiment has been done using reallife data focusing on student’s results in higher-education institution in Malaysia. The results from the new algorithm show that the number of iteration improves tremendously, and the number of iterations is reduced. The improvement rate is around 78%.
format	Article
author	Wan Maseri, Wan Mohd Beg, Abul Hashem Tutut, Herawan K., F.Rabbi
author_facet	Wan Maseri, Wan Mohd Beg, Abul Hashem Tutut, Herawan K., F.Rabbi
author_sort	Wan Maseri, Wan Mohd
title	Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster
title_short	Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster
title_full	Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster
title_fullStr	Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster
title_full_unstemmed	Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster
title_sort	max-d clustering k-means algorithm for autogeneration of centroids and distance of data points cluster
url	http://umpir.ump.edu.my/id/eprint/6871/ http://umpir.ump.edu.my/id/eprint/6871/ http://umpir.ump.edu.my/id/eprint/6871/1/Max-D_clustering_K-means_algorithm_for_Autogeneration.pdf
first_indexed	2023-09-18T22:03:02Z
last_indexed	2023-09-18T22:03:02Z
_version_	1777414525589389312

Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster

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