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JCR Impact Factor: 0.700
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Issues per year: 4
Current issue: May 2024
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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


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Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

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 for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  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
 
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Download PDF pdficon (1,497 KB) | Citation | Downloads: 1,778 | Views: 3,889

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

Cited-By Clarivate Web of Science

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Cited-By SCOPUS

SCOPUS® Times Cited: 20
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Cited-By CrossRef

[1] DP-k-modes: A self-tuning k-modes clustering algorithm, Xie, Juanying, Wang, Mingzhao, Lu, Xiaoxiao, Liu, Xinglin, Grant, Philip W., Pattern Recognition Letters, ISSN 0167-8655, Issue , 2022.
Digital Object Identifier: 10.1016/j.patrec.2022.04.026
[CrossRef]

[2] Diabetes subtypes classification for personalized health care: A review, Omar, Nashuha, Nazirun, Nisha Nadhira, Vijayam, Bhuwaneswaran, Wahab, Asnida Abdul, Bahuri, Hana Ahmad, Artificial Intelligence Review, ISSN 0269-2821, Issue 3, Volume 56, 2023.
Digital Object Identifier: 10.1007/s10462-022-10202-8
[CrossRef]

[3] Two-level clustering of UML class diagrams based on semantics and structure, Ma, Zongmin, Yuan, Zhongchen, Yan, Li, Information and Software Technology, ISSN 0950-5849, Issue , 2021.
Digital Object Identifier: 10.1016/j.infsof.2020.106456
[CrossRef]

[4] Dual-granularity weighted ensemble clustering, Xu, Li, Ding, Shifei, Knowledge-Based Systems, ISSN 0950-7051, Issue , 2021.
Digital Object Identifier: 10.1016/j.knosys.2021.107124
[CrossRef]

[5] End-to-End Hierarchical Reinforcement Learning With Integrated Subgoal Discovery, Pateria, Shubham, Subagdja, Budhitama, Tan, Ah-Hwee, Quek, Chai, IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, Issue 12, Volume 33, 2022.
Digital Object Identifier: 10.1109/TNNLS.2021.3087733
[CrossRef]

[6] A Novel Algorithm Based on Geometric Characteristics for Tree Branch Skeleton Extraction from LiDAR Point Cloud, Yang, Jie, Wen, Xiaorong, Wang, Qiulai, Ye, Jin-Sheng, Zhang, Yanli, Sun, Yuan, Forests, ISSN 1999-4907, Issue 10, Volume 13, 2022.
Digital Object Identifier: 10.3390/f13101534
[CrossRef]

[7] Determining optimal granularity level of modular product with hierarchical clustering and modularity assessment, Li, Zhong-kai, Wang, Shuai, Yin, Wen-wei, Journal of the Brazilian Society of Mechanical Sciences and Engineering, ISSN 1678-5878, Issue 8, Volume 41, 2019.
Digital Object Identifier: 10.1007/s40430-019-1848-y
[CrossRef]

[8] Confidence-Based Simple Graph Convolutional Networks for Face Clustering, Sun, Dengdi, Yang, Kang, Ding, Zhuanlian, IEEE Access, ISSN 2169-3536, Issue , 2022.
Digital Object Identifier: 10.1109/ACCESS.2022.3142922
[CrossRef]

[9] Efficient and Privacy Preserving Clustering Algorithm for Spatiotemporal Data, Mehmood, Abid, Natgunanathan, Iynkaran, Xiang, Yong, International Journal of Information Technology & Decision Making, ISSN 0219-6220, Issue 02, Volume 23, 2024.
Digital Object Identifier: 10.1142/S0219622022500110
[CrossRef]

[10] Fuzzy-Rough Intrigued Harmonic Discrepancy Clustering, Yue, Guanli, Qu, Yanpeng, Yang, Longzhi, Shang, Changjing, Deng, Ansheng, Chao, Fei, Shen, Qiang, IEEE Transactions on Fuzzy Systems, ISSN 1063-6706, Issue 10, Volume 31, 2023.
Digital Object Identifier: 10.1109/TFUZZ.2023.3247912
[CrossRef]

[11] Analysis of the Evolution of the Spanish Labour Market Through Unsupervised Learning, Luna-Romera, Jose Maria, Nunez-Hernandez, Fernando, Martinez-Ballesteros, Maria, Riquelme, Jose C., Usabiaga Ibanez, Carlos, IEEE Access, ISSN 2169-3536, Issue , 2019.
Digital Object Identifier: 10.1109/ACCESS.2019.2935386
[CrossRef]

[12] Genie+OWA: Robustifying hierarchical clustering with OWA-based linkages, Cena, Anna, Gagolewski, Marek, Information Sciences, ISSN 0020-0255, Issue , 2020.
Digital Object Identifier: 10.1016/j.ins.2020.02.025
[CrossRef]

[13] A Hierarchical Attention Network for Cross-Domain Group Recommendation, Liang, Ruxia, Zhang, Qian, Wang, Jianqiang, Lu, Jie, IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, Issue 3, Volume 35, 2024.
Digital Object Identifier: 10.1109/TNNLS.2022.3200480
[CrossRef]

[14] A Survey of Data Mining and Deep Learning in Bioinformatics, Lan, Kun, Wang, Dan-tong, Fong, Simon, Liu, Lian-sheng, Wong, Kelvin K. L., Dey, Nilanjan, Journal of Medical Systems, ISSN 0148-5598, Issue 8, Volume 42, 2018.
Digital Object Identifier: 10.1007/s10916-018-1003-9
[CrossRef]

[15] Automatic Hierarchical Reinforcement Learning for Reusing Service Process Fragments, Yang, Rong, Li, Bing, Liu, Zhengli, IEEE Access, ISSN 2169-3536, Issue , 2021.
Digital Object Identifier: 10.1109/ACCESS.2021.3054852
[CrossRef]

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