Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters

K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means...

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Main Authors:	Wan Maseri, Wan Mohd, Beg, Abul Hashem, Herawan, Tutut, Noraziah, Ahmad
Format:	Article
Language:	English
Published:	IGI Global 2011
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/9328/ http://umpir.ump.edu.my/id/eprint/9328/ http://umpir.ump.edu.my/id/eprint/9328/ http://umpir.ump.edu.my/id/eprint/9328/7/improved-parameterless-k-means_-auto-generation-centroids-and-distance-data-point-clusters%281%29.pdf

id	ump-9328
recordtype	eprints
spelling	ump-93282018-02-05T00:25:43Z http://umpir.ump.edu.my/id/eprint/9328/ Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters Wan Maseri, Wan Mohd Beg, Abul Hashem Herawan, Tutut Noraziah, Ahmad QA75 Electronic computers. Computer science K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for effective and efficient clustering process. The underlined mechanism has been analyzed and experimented. The experimental results show that the number of iteration is reduced to 50% and the run time is lower and constantly based on maximum distance of data points, regardless of how many data points. IGI Global 2011-07 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/9328/7/improved-parameterless-k-means_-auto-generation-centroids-and-distance-data-point-clusters%281%29.pdf Wan Maseri, Wan Mohd and Beg, Abul Hashem and Herawan, Tutut and Noraziah, Ahmad (2011) Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters. International Journal of Information Retrieval Research (IJIRR), 1 (3). pp. 1-14. ISSN 2155-6377 (print); 2155-6385 (online) http://www.igi-global.com/article/improved-parameterless-means/64168 10.4018/ijirr.2011070101
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Wan Maseri, Wan Mohd Beg, Abul Hashem Herawan, Tutut Noraziah, Ahmad Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters
description	K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for effective and efficient clustering process. The underlined mechanism has been analyzed and experimented. The experimental results show that the number of iteration is reduced to 50% and the run time is lower and constantly based on maximum distance of data points, regardless of how many data points.
format	Article
author	Wan Maseri, Wan Mohd Beg, Abul Hashem Herawan, Tutut Noraziah, Ahmad
author_facet	Wan Maseri, Wan Mohd Beg, Abul Hashem Herawan, Tutut Noraziah, Ahmad
author_sort	Wan Maseri, Wan Mohd
title	Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters
title_short	Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters
title_full	Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters
title_fullStr	Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters
title_full_unstemmed	Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters
title_sort	improved parameterless k-means: auto-generation centroids and distance data point clusters
publisher	IGI Global
publishDate	2011
url	http://umpir.ump.edu.my/id/eprint/9328/ http://umpir.ump.edu.my/id/eprint/9328/ http://umpir.ump.edu.my/id/eprint/9328/ http://umpir.ump.edu.my/id/eprint/9328/7/improved-parameterless-k-means_-auto-generation-centroids-and-distance-data-point-clusters%281%29.pdf
first_indexed	2023-09-18T22:07:47Z
last_indexed	2023-09-18T22:07:47Z
_version_	1777414824720859136

Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters

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