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K-Linkage: A New Agglomerative Approach for Hierarchical ClusteringYILDIRIM, P. , BIRANT, D. |
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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
Abstract
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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