A New Assessment of Cluster Tendency Ensemble approach for Data Clustering
Pages 216 - 221
Abstract
The ensemble is an universal machine learning method that is based on the divide-and-conquer principle. The ensemble aims to improve performance of system in terms of processing speed and quality. The assessment of cluster tendency is a method determining whether a considering data-set contains meaningful clusters. Recently, a silhouette-based assessment of cluster tendency method (SACT) has been proposed to simultaneously determine the appropriate number of data clusters and the prototypes. The advantages of SACT are accuracy and less the parameter, while there are limitations in data size and processing speed. In this paper, we proposed an improved SACT method for data clustering. We call eSACT algorithm. Experiments were conducted on synthetic data-sets and color image images. The proposed algorithm exhibited high performance, reliability and accuracy compared to previous proposed algorithms in the assessment of cluster tendency.
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Published In
December 2018
496 pages
ISBN:9781450365390
DOI:10.1145/3287921
Copyright © 2018 ACM.
© 2018 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
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- SOICT: School of Information and Communication Technology - HUST
- NAFOSTED: The National Foundation for Science and Technology Development
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 06 December 2018
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- Research-article
- Research
- Refereed limited
Funding Sources
- Vietnam National Foundation for Science and Technology Development (NAFOSTED)
Conference
SoICT 2018
SoICT 2018: The Ninth International Symposium on Information and Communication Technology
December 6 - 7, 2018
Danang City, Viet Nam
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Overall Acceptance Rate 147 of 318 submissions, 46%
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