Click to open the HelpDesk interface
AECE - Front page banner



JCR Impact Factor: 1.102
JCR 5-Year IF: 0.734
Issues per year: 4
Current issue: Feb 2021
Next issue: May 2021
Avg review time: 53 days


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


2,769,984 unique visits
Since November 1, 2009

Robots online now


SCImago Journal & Country Rank


Anycast DNS Hosting

 Volume 21 (2021)
     »   Issue 1 / 2021
 Volume 20 (2020)
     »   Issue 4 / 2020
     »   Issue 3 / 2020
     »   Issue 2 / 2020
     »   Issue 1 / 2020
 Volume 19 (2019)
     »   Issue 4 / 2019
     »   Issue 3 / 2019
     »   Issue 2 / 2019
     »   Issue 1 / 2019
 Volume 18 (2018)
     »   Issue 4 / 2018
     »   Issue 3 / 2018
     »   Issue 2 / 2018
     »   Issue 1 / 2018
 Volume 17 (2017)
     »   Issue 4 / 2017
     »   Issue 3 / 2017
     »   Issue 2 / 2017
     »   Issue 1 / 2017
  View all issues  


Clarivate Analytics published the InCites Journal Citations Report for 2019. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.102 (1.023 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.734.

Starting on the 15th of June 2020 we wiil introduce a new policy for reviewers. Reviewers who provide timely and substantial comments will receive a discount voucher entitling them to an APC reduction. Vouchers (worth of 25 EUR or 50 EUR, depending on the review quality) will be assigned to reviewers after the final decision of the reviewed paper is given. Vouchers issued to specific individuals are not transferable.

Starting on the 15th of December 2019 all paper authors are required to enter their SCOPUS IDs. You may use the free SCOPUS ID lookup form to find yours in case you don't remember it.

Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

Read More »


  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: 1,221 | Views: 2,389

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

Quick view
Full text preview
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 5] [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.

[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 76] [SCOPUS Times Cited 83]

[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 38] [SCOPUS Times Cited 44]

[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 26] [SCOPUS Times Cited 35]

[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 24]

[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 204] [SCOPUS Times Cited 288]

[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 95] [SCOPUS Times Cited 116]

[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 171] [SCOPUS Times Cited 179]

[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.

[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 2]

[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 6] [SCOPUS Times Cited 9]

[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 124]

[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 14] [SCOPUS Times Cited 16]

[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 32] [SCOPUS Times Cited 44]

[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 44] [SCOPUS Times Cited 51]

[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 25] [SCOPUS Times Cited 28]

[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 81] [SCOPUS Times Cited 88]

[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 23] [SCOPUS Times Cited 26]

[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 52] [SCOPUS Times Cited 64]

[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 12] [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 9] [SCOPUS Times Cited 12]

[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 21]

[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 123] [SCOPUS Times Cited 143]

[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 19] [SCOPUS Times Cited 31]

[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 51]

[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 32] [SCOPUS Times Cited 37]

[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 244]

[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 8]

[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 32] [SCOPUS Times Cited 18]

[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 23] [SCOPUS Times Cited 32]

[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 46] [SCOPUS Times Cited 69]

[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 73] [SCOPUS Times Cited 90]

[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: 1,385 TCR
SCOPUS® Citations for all references: 1,877 TCR

Web of Science® Average Citations per reference: 33 ACR
SCOPUS® Average Citations per reference: 45 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 2021-03-02 00:03 in 302 seconds.

Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2021
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania

All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.

Website loading speed and performance optimization powered by: