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

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
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,497 KB) | Citation | Downloads: 861 | Views: 1,692

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
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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.


References | Cited By  «-- Click to see who has cited this paper

[1] H. Yoon, S. Park, "Determining the structural parameters that affect overall properties of warp knitted fabrics using cluster analysis," Textile Research Journal, vol. 72, no. 11, pp. 1013-1022, 2002.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 8]


[2] P. Prada, A. Curran, K. Furton, "Characteristic human scent compounds trapped on natural and synthetic fabrics as analyzed by SPME-GC/MS," Journal of Forensic Science & Criminology, vol. 1, no. 1, pp. 1-10, 2014.
[CrossRef]


[3] Y. Loewenstein, E. Portugaly, M. Fromer, M. Linial, "Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space," Bioinformatics, vol. 24, no. 13, pp. i41-i49, 2008.
[CrossRef] [Web of Science Times Cited 71] [SCOPUS Times Cited 79]


[4] D. Wei, Q. Jiang, Y. Wei, S. Wang, "A novel hierarchical clustering algorithm for gene sequences," BMC Bioinformatics, vol. 13, no. 174, pp. 1-15, 2012.
[CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 38]


[5] Y. Bang, C. Lee, "Fuzzy time series prediction using hierarchical clustering algorithms," Expert Systems with Applications, vol. 38, no. 4, pp. 4312-4325, 2011.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 32]


[6] H. Gao, J. Jiang, L. She, Y. Fu, "A new agglomerative hierarchical clustering algorithm implementation based on the Map Reduce framework," International Journal of Digital Content Technology and its Applications, vol. 4, no. 3, pp. 95-100, 2010.
[CrossRef] [SCOPUS Times Cited 20]


[7] S. Horng, M. Su, Y. Chen, T. Kao, R. Chen, J. Lai, C. Perkasa, "A novel intrusion detection system based on hierarchical clustering and support vector machines," Expert Systems with Applications, vol. 38, no. 1, pp. 306-313, 2011.
[CrossRef] [Web of Science Times Cited 168] [SCOPUS Times Cited 243]


[8] J. Almeida, L. Barbosa, A. Pais, S. Formosinho, "Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering," Chemometrics and Intelligent Laboratory Systems, vol. 87, no. 2, pp. 208-217, 2007.
[CrossRef] [Web of Science Times Cited 85] [SCOPUS Times Cited 104]


[9] S. Deininger, M. Ebert, A. Fu¨tterer, M. Gerhard, C. Ro¨cken, "MALDI imaging combined with hierarchical clustering as a new tool for the interpretation of complex human cancers," Journal of Proteome Research, vol. 7, no. 12, pp. 5230-5236, 2008.
[CrossRef] [Web of Science Times Cited 163] [SCOPUS Times Cited 174]


[10] A. Shalom, M. Dash, "Efficient partitioning based hierarchical agglomerative clustering using graphics accelerators with Cuda," International Journal of Artificial Intelligence & Applications, vol. 4, no. 2, pp. 13-33, 2013.
[CrossRef]


[11] H. A. Dalbouh, N. M. Norwawi, "Bidirectional agglomerative hierarchical clustering using AVL tree algorithm," International Journal of Computer Science Issues, vol. 8, no. 5, pp. 95-102, 2011.

[12] E. Althaus, A. Hildebrandt, A. K. Hildebrandt, "A Greedy algorithm for hierarchical complete linkage clustering," in International Conference on Algorithms for Computational Biology, Tarragona, 2014, pp. 25-34.
[CrossRef] [SCOPUS Times Cited 1]


[13] A. Mamun, R. Aseltine, S. Rajasekaran, "Efficient record linkage algorithms using complete linkage clustering," PLOS ONE, vol. 11, no. 4, pp. 1-21, 2016.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 6]


[14] O. Yim, K. Ramdeen, "Hierarchical Cluster Analysis: Comparison of three linkage measures and application to psychological data," The Quantitative Methods for Psychology, vol. 11, no. 1, pp. 8-21, 2015.
[CrossRef] [Web of Science Times Cited 72]


[15] Y. Li, L. R. Liang, " Hierarchical clustering of features on categorical data of biomedical applications," in Proceedings of the ISCA 21st International Conference on Computer Applications in Industry and Engineering, Hawaii, 2008.

[16] E. Nasibov, C. Kandemir-Cavas, "OWA-based linkage method in hierarchical clustering: Application on phylogenetic trees," Expert Systems with Applications, vol. 38, no. 10, pp. 12684-12690, 2011.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 13]


[17] S. Hirano, X. G. Sun, S. Tsumoto, "Comparison of clustering methods for clinical databases," Information Sciences, vol. 159, no. 3-4, pp. 155-165, 2004.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 41]


[18] J. Bien, R. Tibshirani, "Hierarchical clustering with prototypes via minimax linkage," Journal of the American Statistical Association, vol. 106, no. 495, pp. 1075-1084, 2011.
[CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 43]


[19] M. Gagolewski, M. Bartoszuk, A. Cena, "Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm," Information Sciences, vol. 363, pp. 8-23, 2016.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 23]


[20] S. Dasgupta, P. Long, "Performance guarantees for hierarchical clustering," Journal of Computer and System Sciences, vol. 70, no. 4, pp. 555-569, 2005.
[CrossRef] [Web of Science Times Cited 71] [SCOPUS Times Cited 78]


[21] J. Wu, H. Xiong, J. Chen, "Towards understanding hierarchical clustering: A data distribution perspective," Neurocomputing, vol. 72, no. 10-12, pp. 2319-2330, 2009.
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 25]


[22] A. Mirzaei, M. Rahmati, "A novel hierarchical-clustering-combination scheme based on fuzzy-similarity relations," IEEE Transactions on Fuzzy Systems, vol. 18, no. 1, pp. 27-39, 2010.
[CrossRef] [Web of Science Times Cited 47] [SCOPUS Times Cited 57]


[23] P. Contreras, F. Murtagh, "Fast, linear time hierarchical clustering using the Baire metric," Journal of Classification, vol. 29, no. 2, pp. 118-143, 2012.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 16]


[24] A. Barirani, B. Agard, C. Beaudry, "Competence maps using agglomerative hierarchical clustering," Journal of Intelligent Manufacturing, vol. 24, no. 2, pp. 373-384, 2011.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 10]


[25] H. Clifford, F. Wessely, S. Pendurthi, R. Emes, "Comparison of clustering methods for investigation of genome-wide methylation array data," Frontiers in Genetics, vol. 2, no. 88, pp. 1-11, 2011.
[CrossRef] [SCOPUS Times Cited 16]


[26] Y. M. Yacob, H. A. M. Sakim, N. A. M. Isa, "Decision tree-based feature ranking using Manhattan hierarchical cluster criterion," International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, vol. 6, no. 2, pp. 765-771, 2012.

[27] A. Bouguettaya, Q. Yu, X. Liu, X. Zhou, A. Song, "Efficient agglomerative hierarchical clustering," Expert Systems with Applications, vol. 42, no. 5, pp. 2785-2797, 2015.
[CrossRef] [Web of Science Times Cited 80] [SCOPUS Times Cited 96]


[28] M. Luczak, "Hierarchical clustering of time series data with parametric derivative dynamic time warping," Expert Systems with Applications, vol. 62, pp. 116-130, 2016.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 22]


[29] D. Eppstein, "Fast hierarchical clustering and other applications of dynamic closest pairs," Journal of Experimental Algorithmics, vol. 5, p. 1-10, 2000.
[CrossRef] [SCOPUS Times Cited 49]


[30] Y. Lu, Y. Wan, "PHA: A fast potential-based hierarchical agglomerative clustering method," Pattern Recognition, vol. 46, no. 5, pp. 1227-1239, 2013.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 31]


[31] D. Müllner, "fastcluster: Fast hierarchical, agglomerative clustering routines for R and Python," Journal of Statistical Software, vol. 53, no. 9, 2013.
[CrossRef] [SCOPUS Times Cited 179]


[32] E. Masciari, G. M. Mazzeo, C. Zaniolo, "A new, fast and accurate algorithm for hierarchical clustering on Euclidean distances," in Pacific-Asia Conference on Knowledge Discovery and Data Mining, Gold Coast, 2013.
[CrossRef] [SCOPUS Times Cited 7]


[33] I. Davidson and S. S. Ravi, "Towards efficient and improved hierarchical clustering with instance and cluster level constraints", Technical Report, Department of Computer Science, University at Albany, 2005.

[34] S. Bobdiya, K. Patidar, "An efficient ensemble based hierarchical clustering algorithm," International Journal of Emerging Technology and Advanced Engineering, vol. 4, no. 7, pp. 661-666, 2014.

[35] L. Zheng, T. Li, C. Ding, "A framework for hierarchical ensemble clustering," Acm Transactions on Knowledge Discovery from Data, vol. 9, no. 2, 2014.
[CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 15]


[36] Z. Chen, S. Zhou, J. Luo, "A robust ant colony optimization for continuous functions," Expert Systems with Applications, vol. 81, pp. 309-320, 2017.
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 24]


[37] J. Vašcák, "Adaptation of fuzzy cognitive maps by migration algorithms," Kybernetes, vol. 41, no. 3, pp. 429-443, 2012.
[CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 59]


[38] R. Precup, M. Sabau, E. M. Petriu, "Nature-inspired optimal tuning of input membership functions of Takagi-Sugeno-Kang fuzzy models for anti-lock braking systems," Applied Soft Computing, vol. 27, pp. 575-589, 2015.
[CrossRef] [Web of Science Times Cited 61] [SCOPUS Times Cited 76]


[39] S. Vrkalovic, T. Teban, I. Borlea, "Stable Takagi-Sugeno fuzzy control designed by optimization," International Journal of Artificial Intelligence, vol. 15, no. 2, pp. 17-29, 2017.

[40] C. D. Manning, P. Raghavan, H. Schütze, "Hierarchical clustering", An Introduction to Information Retrieval, pp. 377-402, Cambridge University Press, 2012.

[41] B. Walter, K. Bala, M. Kulkarni, K. Pingali, "Fast agglomerative clustering for rendering," in The IEEE Symposium on Interactive Ray Tracing, Los Angeles, 2008.



References Weight

Web of Science® Citations for all references: 1,147 TCR
SCOPUS® Citations for all references: 1,585 TCR

Web of Science® Average Citations per reference: 27 ACR
SCOPUS® Average Citations per reference: 38 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 2020-01-24 07:05 in 227 seconds.




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