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Triangular kernel nearest-neighbor-based clustering algorithm for discovering true clusters

Clustering is a powerful exploratory technique for extracting the knowledge of given data. Several clustering techniques that have been proposed require predetermined number of clusters. However, the triangular kernel-nearest neighbor-based clustering (TKNN) has been proven able to determine the num...

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Main Authors: Musdholifah, Aina, Mohd Hashim, Siti Zaiton
Format: Conference or Workshop Item
Published: 2013
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/51381/
http://dx.doi.org/10.1007/978-3-642-36778-6_11
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spelling my.utm.513812017-07-18T07:58:07Z http://eprints.utm.my/id/eprint/51381/ Triangular kernel nearest-neighbor-based clustering algorithm for discovering true clusters Musdholifah, Aina Mohd Hashim, Siti Zaiton QA75 Electronic computers. Computer science Clustering is a powerful exploratory technique for extracting the knowledge of given data. Several clustering techniques that have been proposed require predetermined number of clusters. However, the triangular kernel-nearest neighbor-based clustering (TKNN) has been proven able to determine the number and member of clusters automatically. TKNN provides good solutions for clustering non-spherical and high-dimensional data without prior knowledge of data labels. On the other hand, there is no definite measure to evaluate the accuracy of the clustering result. In order to evaluate the performance of the proposed TKNN clustering algorithm, we utilized various benchmark classification datasets. Thus, TKNN is proposed for discovering true clusters with arbitrary shape, size and density contained in the datasets. The experimental results on benched-mark datasets showed the effectiveness of our technique. Our proposed TKNN achieved more accurate clustering results and required less time processing compared with k-means, ILGC, DBSCAN and KFCM. © 2013 Springer-Verlag. 2013 Conference or Workshop Item PeerReviewed Musdholifah, Aina and Mohd Hashim, Siti Zaiton (2013) Triangular kernel nearest-neighbor-based clustering algorithm for discovering true clusters. In: PAKDD 2012 International Workshops: 3rd Data Mining for Healthcare Management, DMHM 2012, Multi-View Data, High-Dimensionality, External Knowledge: Striving for a Unified Approach to Clustering, 3Clust 2012, GeoDoc 2012 and 2nd DSDM 2012, 29 May 2012 through 1 June 2012, Kuala Lumpur, Malaysia. http://dx.doi.org/10.1007/978-3-642-36778-6_11
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Musdholifah, Aina
Mohd Hashim, Siti Zaiton
Triangular kernel nearest-neighbor-based clustering algorithm for discovering true clusters
description Clustering is a powerful exploratory technique for extracting the knowledge of given data. Several clustering techniques that have been proposed require predetermined number of clusters. However, the triangular kernel-nearest neighbor-based clustering (TKNN) has been proven able to determine the number and member of clusters automatically. TKNN provides good solutions for clustering non-spherical and high-dimensional data without prior knowledge of data labels. On the other hand, there is no definite measure to evaluate the accuracy of the clustering result. In order to evaluate the performance of the proposed TKNN clustering algorithm, we utilized various benchmark classification datasets. Thus, TKNN is proposed for discovering true clusters with arbitrary shape, size and density contained in the datasets. The experimental results on benched-mark datasets showed the effectiveness of our technique. Our proposed TKNN achieved more accurate clustering results and required less time processing compared with k-means, ILGC, DBSCAN and KFCM. © 2013 Springer-Verlag.
format Conference or Workshop Item
author Musdholifah, Aina
Mohd Hashim, Siti Zaiton
author_facet Musdholifah, Aina
Mohd Hashim, Siti Zaiton
author_sort Musdholifah, Aina
title Triangular kernel nearest-neighbor-based clustering algorithm for discovering true clusters
title_short Triangular kernel nearest-neighbor-based clustering algorithm for discovering true clusters
title_full Triangular kernel nearest-neighbor-based clustering algorithm for discovering true clusters
title_fullStr Triangular kernel nearest-neighbor-based clustering algorithm for discovering true clusters
title_full_unstemmed Triangular kernel nearest-neighbor-based clustering algorithm for discovering true clusters
title_sort triangular kernel nearest-neighbor-based clustering algorithm for discovering true clusters
publishDate 2013
url http://eprints.utm.my/id/eprint/51381/
http://dx.doi.org/10.1007/978-3-642-36778-6_11
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score 13.211869

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