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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2017 - 10


K-Linkage: A New Agglomerative Approach for Hierarchical Clustering

YILDIRIM, P. See more information about YILDIRIM, P. on SCOPUS See more information about YILDIRIM, P. on IEEExplore See more information about YILDIRIM, P. on Web of Science, BIRANT, D. See more information about BIRANT, D. on SCOPUS See more information about BIRANT, D. on SCOPUS See more information about BIRANT, D. on Web of Science
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Download PDF pdficon (1,497 KB) | Citation | Downloads: 1,362 | Views: 2,662

Author keywords
clustering, data mining, data processing, knowledge discovery, unsupervised learning

References keywords
clustering(33), hierarchical(31), applications(11), systems(9), agglomerative(8), fast(7), data(7), algorithm(7), linkage(6), jeswa(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-11-30
Volume 17, Issue 4, Year 2017, On page(s): 77 - 88
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.04010
Web of Science Accession Number: 000417674300010
SCOPUS ID: 85035794377

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In agglomerative hierarchical clustering, the traditional approaches of computing cluster distances are single, complete, average and centroid linkages. However, single-link and complete-link approaches cannot always reflect the true underlying relationship between clusters, because they only consider just a single pair between two clusters. This situation may promote the formation of spurious clusters. To overcome the problem, this paper proposes a novel approach, named k-Linkage, which calculates the distance by considering k observations from two clusters separately. This article also introduces two novel concepts: k-min linkage (the average of k closest pairs) and k-max linkage (the average of k farthest pairs). In the experimental studies, the improved hierarchical clustering algorithm based on k-Linkage was executed on five well-known benchmark datasets with varying k values to demonstrate its efficiency. The results show that the proposed k-Linkage method can often produce clusters with better accuracy, compared to the single, complete, average and centroid linkages.

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References Weight

Web of Science® Citations for all references: 1,514 TCR
SCOPUS® Citations for all references: 2,006 TCR

Web of Science® Average Citations per reference: 36 ACR
SCOPUS® Average Citations per reference: 48 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-09-11 14:50 in 210 seconds.

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