Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.825
JCR 5-Year IF: 0.752
SCOPUS CiteScore: 2.5
Issues per year: 4
Current issue: Aug 2022
Next issue: Nov 2022
Avg review time: 76 days
Avg accept to publ: 48 days
APC: 300 EUR


PUBLISHER

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


TRAFFIC STATS

1,972,820 unique visits
787,556 downloads
Since November 1, 2009



Robots online now
PetalBot


SCOPUS CiteScore

SCOPUS CiteScore


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 22 (2022)
 
     »   Issue 3 / 2022
 
     »   Issue 2 / 2022
 
     »   Issue 1 / 2022
 
 
 Volume 21 (2021)
 
     »   Issue 4 / 2021
 
     »   Issue 3 / 2021
 
     »   Issue 2 / 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
 
 
  View all issues  








LATEST NEWS

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering in 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

2021-Jun-06
SCOPUS published the CiteScore for 2020, computed by using an improved methodology, counting the citations received in 2017-2020 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering in 2020 is 2.5, better than all our previous results.

2021-Apr-15
Release of the v3 version of AECE Journal website. We moved to a new server and implemented the latest cryptographic protocols to assure better compatibility with the most recent browsers. Our website accepts now only TLS 1.2 and TLS 1.3 secure connections.

Read More »


    
 

  4/2017 - 10

 HIGH-IMPACT PAPER 

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
 
View the paper record and citations in View the paper record and citations in Google Scholar
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: 1,596 | Views: 3,035

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
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 6] [SCOPUS Times Cited 9]


[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 89] [SCOPUS Times Cited 96]


[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 52] [SCOPUS Times Cited 61]


[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 30] [SCOPUS Times Cited 39]


[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 26]


[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 243] [SCOPUS Times Cited 343]


[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 117] [SCOPUS Times Cited 137]


[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 176] [SCOPUS Times Cited 189]


[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 3]


[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 9] [SCOPUS Times Cited 11]


[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]


[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 15] [SCOPUS Times Cited 18]


[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 37] [SCOPUS Times Cited 47]


[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 67] [SCOPUS Times Cited 81]


[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 37] [SCOPUS Times Cited 38]


[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 99] [SCOPUS Times Cited 107]


[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 27] [SCOPUS Times Cited 30]


[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 56] [SCOPUS Times Cited 67]


[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 13] [SCOPUS Times Cited 17]


[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 13]


[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 26]


[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 190] [SCOPUS Times Cited 223]


[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 37] [SCOPUS Times Cited 52]


[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 55]


[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 38] [SCOPUS Times Cited 43]


[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 339]


[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 10]


[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 38] [SCOPUS Times Cited 24]


[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 31] [SCOPUS Times Cited 41]


[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 49] [SCOPUS Times Cited 74]


[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 82] [SCOPUS Times Cited 95]


[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: 1,547 TCR
SCOPUS® Citations for all references: 2,314 TCR

Web of Science® Average Citations per reference: 37 ACR
SCOPUS® Average Citations per reference: 55 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 2022-09-22 17:15 in 212 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 ©2001-2022
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: