MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters

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, Herawan, Tutut, Fazley Rabbi, Khandakar
Format:	Article
Language:	English
Published:	Springer, Berlin, Heidelberg 2012
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/27004/ http://umpir.ump.edu.my/id/eprint/27004/ http://umpir.ump.edu.my/id/eprint/27004/ http://umpir.ump.edu.my/id/eprint/27004/1/MaxD%20K-Means-%20A%20clustering%20algorithm%20for%20auto-generation%20of%20centroids.pdf

id	ump-27004
recordtype	eprints
spelling	ump-270042020-02-26T08:35:34Z http://umpir.ump.edu.my/id/eprint/27004/ MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters Wan Maseri, Wan Mohd Beg, Abul Hashem Herawan, Tutut Fazley Rabbi, Khandakar 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, we propose 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. The results from the new algorithm show that the number of iteration improves tremendously, and the number of iterations is reduced by confirming an improvement rate is up to 78%. Springer, Berlin, Heidelberg 2012 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/27004/1/MaxD%20K-Means-%20A%20clustering%20algorithm%20for%20auto-generation%20of%20centroids.pdf Wan Maseri, Wan Mohd and Beg, Abul Hashem and Herawan, Tutut and Fazley Rabbi, Khandakar (2012) MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters. Communications in Computer and Information Science, 316. pp. 192-199. https://doi.org/10.1007/978-3-642-34289-9_22 https://doi.org/10.1007/978-3-642-34289-9_22
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 Herawan, Tutut Fazley Rabbi, Khandakar MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters
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, we propose 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. The results from the new algorithm show that the number of iteration improves tremendously, and the number of iterations is reduced by confirming an improvement rate is up to 78%.
format	Article
author	Wan Maseri, Wan Mohd Beg, Abul Hashem Herawan, Tutut Fazley Rabbi, Khandakar
author_facet	Wan Maseri, Wan Mohd Beg, Abul Hashem Herawan, Tutut Fazley Rabbi, Khandakar
author_sort	Wan Maseri, Wan Mohd
title	MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters
title_short	MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters
title_full	MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters
title_fullStr	MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters
title_full_unstemmed	MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters
title_sort	maxd k-means: a clustering algorithm for auto-generation of centroids and distance of data points in clusters
publisher	Springer, Berlin, Heidelberg
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/27004/ http://umpir.ump.edu.my/id/eprint/27004/ http://umpir.ump.edu.my/id/eprint/27004/ http://umpir.ump.edu.my/id/eprint/27004/1/MaxD%20K-Means-%20A%20clustering%20algorithm%20for%20auto-generation%20of%20centroids.pdf
first_indexed	2023-09-18T22:42:21Z
last_indexed	2023-09-18T22:42:21Z
_version_	1777416999502086144

MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters

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