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Journal articles on the topic 'Hierarchical agglomerative clustering'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Bouguettaya, Athman, Qi Yu, Xumin Liu, Xiangmin Zhou, and Andy Song. "Efficient agglomerative hierarchical clustering." Expert Systems with Applications 42, no. 5 (2015): 2785–97. http://dx.doi.org/10.1016/j.eswa.2014.09.054.

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2

Bogucharskiy, Sergiy I., and Sergey V. Mashtalir. "HIERARCHICAL AGGLOMERATIVE CLUSTERING IN MULTIMEDIA DATABASE." ELECTRICAL AND COMPUTER SYSTEMS 19, no. 95 (2015): 239–42. http://dx.doi.org/10.15276/eltecs.19.95.2015.53.

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3

Zhang, Xiaolu, and Zeshui Xu. "Hesitant fuzzy agglomerative hierarchical clustering algorithms." International Journal of Systems Science 46, no. 3 (2013): 562–76. http://dx.doi.org/10.1080/00207721.2013.797037.

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4

Barirani, Ahmad, Bruno Agard, and Catherine Beaudry. "Competence maps using agglomerative hierarchical clustering." Journal of Intelligent Manufacturing 24, no. 2 (2011): 373–84. http://dx.doi.org/10.1007/s10845-011-0600-y.

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5

Li, Fang, and Qun Xiong Zhu. "Research on NMF Based Hierarchical Clustering Methods." Key Engineering Materials 439-440 (June 2010): 1306–11. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.1306.

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LSI based hierarchical agglomerative clustering algorithm is studied. Aiming to the problems of LSI based hierarchical agglomerative clustering method, NMF based hierarchical clustering method is proposed and analyzed. Two ways of implementing NMF based method are introduced. Finally the result of two groups of experiment based on the TanCorp document corpora show that the method proposed is effective.

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6

Lerato, Lerato, and Thomas Niesler. "Clustering Acoustic Segments Using Multi-Stage Agglomerative Hierarchical Clustering." PLOS ONE 10, no. 10 (2015): e0141756. http://dx.doi.org/10.1371/journal.pone.0141756.

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7

Eyal Salman, Hamzeh, Mustafa Hammad, Abdelhak-Djamel Seriai, and Ahed Al-Sbou. "Semantic Clustering of Functional Requirements Using Agglomerative Hierarchical Clustering." Information 9, no. 9 (2018): 222. http://dx.doi.org/10.3390/info9090222.

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Software applications have become a fundamental part in the daily work of modern society as they meet different needs of users in different domains. Such needs are known as software requirements (SRs) which are separated into functional (software services) and non-functional (quality attributes). The first step of every software development project is SR elicitation. This step is a challenge task for developers as they need to understand and analyze SRs manually. For example, the collected functional SRs need to be categorized into different clusters to break-down the project into a set of sub

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8

Widyawati, Widyawati, Wawan Laksito Yuly Saptomo, and Yustina Retno Wahyu Utami. "Penerapan Agglomerative Hierarchical Clustering Untuk Segmentasi Pelanggan." Jurnal Ilmiah SINUS 18, no. 1 (2020): 75. http://dx.doi.org/10.30646/sinus.v18i1.448.

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As more businesses emerge, companies need to have the right marketing strategy to provide the best service to customers. The first step is to know the type of customer and make appropriate marketing strategies according to the type of customer. In this research, it is proposed for clustering customers so that an appropriate strategy for that customer group can be determined. The method used for cluster formation uses Agglomerative Hierarchical Clustering with Average Linkage approach and distance determination using Manhattan Distance. The variables in this research are Recency, Frequency, and

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9

S., Sarika, and Mukesh Rawat. "Design and Comparison of Agglomerative Hierarchical Clustering." International Journal of Computer Applications 172, no. 10 (2017): 1–5. http://dx.doi.org/10.5120/ijca2017914993.

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10

Erman, Nusa, Ales Korosec, and Jana Suklan. "PERFORMANCE OF SELECTED AGGLOMERATIVE HIERARCHICAL CLUSTERING METHODS." Innovative Issues and Approaches in Social Sciences 8, no. 1 (2015): 180–204. http://dx.doi.org/10.12959/issn.1855-0541.iiass-2015-no1-art11.

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11

Ieva, Carlo, Arnaud Gotlieb, Souhila Kaci, and Nadjib Lazaar. "Discovering Program Topoi via Hierarchical Agglomerative Clustering." IEEE Transactions on Reliability 67, no. 3 (2018): 758–70. http://dx.doi.org/10.1109/tr.2018.2828135.

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12

Hamasuna, Yukihiro, Yasunori Endo, and Sadaaki Miyamoto. "On Agglomerative Hierarchical Clustering Using Clusterwise Tolerance Based Pairwise Constraints." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 1 (2012): 174–79. http://dx.doi.org/10.20965/jaciii.2012.p0174.

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This paper presents semi-supervised agglomerative hierarchical clustering algorithm using clusterwise tolerance based pairwise constraints. In semi-supervised clustering, pairwise constraints, that is, must-link and cannot-link, are frequently used in order to improve clustering properties. From that sense, we will propose another way named clusterwise tolerance based pairwise constraints to handle must-link and cannot-link constraints inL2-space. In addition, we will propose semi-supervised agglomerative hierarchical clustering algorithm based on it. We will, moreover, show the effectiveness

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13

Gilpin, Andrew R. "APCLUST: Agglomerative Hierarchical Clustering Analysis Program for Microcomputers." American Statistician 40, no. 1 (1986): 53. http://dx.doi.org/10.2307/2683124.

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14

Murray, G. Craig, Jimmy Lin, and Abdur Chowdhury. "Identification of user sessions with hierarchical agglomerative clustering." Proceedings of the American Society for Information Science and Technology 43, no. 1 (2007): 1–9. http://dx.doi.org/10.1002/meet.14504301312.

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15

Yager, R. R. "Intelligent control of the hierarchical agglomerative clustering process." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 30, no. 6 (2000): 835–45. http://dx.doi.org/10.1109/3477.891145.

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16

Al-Dabooni, Seaar, and Donald Wunsch. "Model Order Reduction Based on Agglomerative Hierarchical Clustering." IEEE Transactions on Neural Networks and Learning Systems 30, no. 6 (2019): 1881–95. http://dx.doi.org/10.1109/tnnls.2018.2873196.

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17

Takeuchi, Akinobu, Takayuki Saito, and Hiroshi Yadohisa. "Asymmetric Agglomerative Hierarchical Clustering Algorithms and Their Evaluations." Journal of Classification 24, no. 1 (2007): 123–43. http://dx.doi.org/10.1007/s00357-007-0002-1.

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18

Kruse, C., P. Eiken, and P. Vestergaard. "Clinical fracture risk evaluated by hierarchical agglomerative clustering." Osteoporosis International 28, no. 3 (2016): 819–32. http://dx.doi.org/10.1007/s00198-016-3828-8.

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19

Vach, Werner, and Paul O. Degens. "A new approach to isotonic agglomerative hierarchical clustering." Journal of Classification 8, no. 2 (1991): 217–37. http://dx.doi.org/10.1007/bf02616240.

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20

Februariyanti, Herny, Jati Sasongko Wibowo, Dwi Budi Santoso, and Muji Sukur. "ANALISIS KECENDERUNGAN INFORMASI MENGGUNAKAN ALGORITMA HIERARCHICAL AGGLOMERATIVE CLUSTERING." I N F O R M A T I K A 13, no. 1 (2021): 9. http://dx.doi.org/10.36723/juri.v13i1.247.

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21

B, Karthikeyan. "A Comparative Study on K-Means Clustering and Agglomerative Hierarchical Clustering." International Journal of Emerging Trends in Engineering Research 8, no. 5 (2020): 1600–1604. http://dx.doi.org/10.30534/ijeter/2020/20852020.

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22

Alshaikhdeeb, Basel, and Kamsuriah Ahmad. "Integrating Correlation Clustering and Agglomerative Hierarchical Clustering for Holistic Schema Matching." Journal of Computer Science 11, no. 3 (2015): 484–89. http://dx.doi.org/10.3844/jcssp.2015.484.489.

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23

Abdurrahman, Ginanjar. "Clustering Data Kredit Bank Menggunakan Algoritma Agglomerative Hierarchical Clustering Average Linkage." JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) 4, no. 1 (2019): 13. http://dx.doi.org/10.32528/justindo.v4i1.2418.

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24

Górecki, J., M. Hofert, and M. Holeňa. "Kendall’s tau and agglomerative clustering for structure determination of hierarchical Archimedean copulas." Dependence Modeling 5, no. 1 (2017): 75–87. http://dx.doi.org/10.1515/demo-2017-0005.

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Abstract Several successful approaches to structure determination of hierarchical Archimedean copulas (HACs) proposed in the literature rely on agglomerative clustering and Kendall’s correlation coefficient. However, there has not been presented any theoretical proof justifying such approaches. This work fills this gap and introduces a theorem showing that, given the matrix of the pairwise Kendall correlation coefficients corresponding to a HAC, its structure can be recovered by an agglomerative clustering technique.

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25

Hamasuna, Yukihiro, Shusuke Nakano, Ryo Ozaki, and and Yasunori Endo. "Cluster Validity Measures Based Agglomerative Hierarchical Clustering for Network Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 3 (2019): 577–83. http://dx.doi.org/10.20965/jaciii.2019.p0577.

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The Louvain method is a method of agglomerative hierarchical clustering (AHC) that uses modularity as the merging criterion. Modularity is an evaluation measure for network partitions. Cluster validity measures are also used to evaluate cluster partitions and to determine the optimal number of clusters. Several cluster validity measures are constructed considering the geometric features of clusters. These measures and modularity are considered to be the same concept in the viewpoint of evaluating cluster partitions. In this paper, cluster validity measures based agglomerative hierarchical clus

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26

YILDIRIM, P., and D. BIRANT. "K-Linkage: A New Agglomerative Approach for Hierarchical Clustering." Advances in Electrical and Computer Engineering 17, no. 4 (2017): 77–88. http://dx.doi.org/10.4316/aece.2017.04010.

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27

Lu, Yonggang, and Yi Wan. "PHA: A fast potential-based hierarchical agglomerative clustering method." Pattern Recognition 46, no. 5 (2013): 1227–39. http://dx.doi.org/10.1016/j.patcog.2012.11.017.

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28

Saad, Fathi H. "Comparison of Hierarchical Agglomerative Algorithms for Clustering Medical Documents." International Journal of Software Engineering & Applications 3, no. 3 (2012): 1–15. http://dx.doi.org/10.5121/ijsea.2012.3301.

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29

Dani, Andrea Tri Rian, Sri Wahyuningsih, and Nanda Arista Rizki. "Penerapan Hierarchical Clustering Metode Agglomerative pada Data Runtun Waktu." Jambura Journal of Mathematics 1, no. 2 (2019): 64–78. http://dx.doi.org/10.34312/jjom.v1i2.2354.

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30

Jatain, Aman, Arpita Nagpal, and Deepti Gaur. "Agglomerative Hierarchical Approach for Clustering Components of Similar Reusability." International Journal of Computer Applications 68, no. 2 (2013): 33–37. http://dx.doi.org/10.5120/11553-6832.

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31

Demir, Ali, and H. Ertan Cetingul. "Sequential Hierarchical Agglomerative Clustering of White Matter Fiber Pathways." IEEE Transactions on Biomedical Engineering 62, no. 6 (2015): 1478–89. http://dx.doi.org/10.1109/tbme.2015.2391913.

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32

Liu, Hongtao, Linghu Fen, Jie Jian, and Long Chen. "Overlapping Community Discovery Algorithm Based on Hierarchical Agglomerative Clustering." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 03 (2017): 1850008. http://dx.doi.org/10.1142/s0218001418500088.

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Overlapping community is a response to the real network structure in social networks and in real society in order to solve the problems such as the parameters of the existing overlapping community discovery algorithm being too large, excessive overlap and no guarantee of stability of multiple runs. In this paper, the method of calculating the node degree of membership was proposed, and an overlapping community discovery algorithm based on the local optimal expansion cohesion idea was designed. Firstly, the initial core community was constructed with the highest importance node and its neighbor

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33

Hussain, Tasawar, and Sohail Asghar. "Chi-square based hierarchical agglomerative clustering for web sessionization." Journal of the National Science Foundation of Sri Lanka 44, no. 2 (2016): 211. http://dx.doi.org/10.4038/jnsfsr.v44i2.8002.

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34

Koga, Hisashi, Tetsuo Ishibashi, and Toshinori Watanabe. "Fast agglomerative hierarchical clustering algorithm using Locality-Sensitive Hashing." Knowledge and Information Systems 12, no. 1 (2006): 25–53. http://dx.doi.org/10.1007/s10115-006-0027-5.

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35

Ramos Emmendorfer, Leonardo, and Anne Magaly de Paula Canuto. "A generalized average linkage criterion for Hierarchical Agglomerative Clustering." Applied Soft Computing 100 (March 2021): 106990. http://dx.doi.org/10.1016/j.asoc.2020.106990.

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36

Khan, Naila Habib, Awais Adnan, and Sadia Basar. "Urdu ligature recognition using multi-level agglomerative hierarchical clustering." Cluster Computing 21, no. 1 (2017): 503–14. http://dx.doi.org/10.1007/s10586-017-0916-2.

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37

Fernández, Alberto, and Sergio Gómez. "Solving Non-Uniqueness in Agglomerative Hierarchical Clustering Using Multidendrograms." Journal of Classification 25, no. 1 (2008): 43–65. http://dx.doi.org/10.1007/s00357-008-9004-x.

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38

Vinothkumar, K., and M. P. Selvan. "Hierarchical Agglomerative Clustering Algorithm method for distributed generation planning." International Journal of Electrical Power & Energy Systems 56 (March 2014): 259–69. http://dx.doi.org/10.1016/j.ijepes.2013.11.021.

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39

Pratikto, Ridzki Okta, and Natalia Damastuti. "Klasterisasi Menggunakan Agglomerative Hierarchical Clustering Untuk Memodelkan Wilayah Banjir." JOINTECS (Journal of Information Technology and Computer Science) 6, no. 1 (2021): 13. http://dx.doi.org/10.31328/jointecs.v6i1.1473.

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40

Takumi, Satoshi, and Sadaaki Miyamoto. "Nearest Prototype and Nearest Neighbor Clustering with Twofold Memberships Based on Inductive Property." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 4 (2013): 504–10. http://dx.doi.org/10.20965/jaciii.2013.p0504.

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The aim of this paper is to study methods of twofold membership clustering using the nearest prototype and nearest neighbor. The former uses theK-means, whereas the latter extends the single linkage in agglomerative hierarchical clustering. The concept of inductive clustering is moreover used for the both methods, which means that natural classification rules are derived as the results of clustering, a typical example of which is the Voronoi regions inK-means clustering. When the rule of nearest prototype allocation inK-means is replaced by nearest neighbor classification, we have inductive cl

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41

Naeem, Arshia, Mariam Rehman, Maria Anjum, and Muhammad Asif. "Development of an Efficient Hierarchical Clustering Analysis using an Agglomerative Clustering Algorithm." Current Science 117, no. 6 (2019): 1045. http://dx.doi.org/10.18520/cs/v117/i6/1045-1053.

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42

KHAN, LATIFUR, and FENG LUO. "HIERARCHICAL CLUSTERING FOR COMPLEX DATA." International Journal on Artificial Intelligence Tools 14, no. 05 (2005): 791–809. http://dx.doi.org/10.1142/s0218213005002399.

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In this paper we introduce a new tree-structured self-organizing neural network called a dynamical growing self-organizing tree (DGSOT). This DGSOT algorithm constructs a hierarchy from top to bottom by division. At each hierarchical level, the DGSOT optimizes the number of clusters, from which the proper hierarchical structure of the underlying data set can be found. We propose a K-level up distribution (KLD) mechanism. This KLD scheme increases the scope for data distribution in the hierarchy, which allows the data mis-clustered in the early stages to be re-evaluated at a later stage increas

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43

ZHAO, YUXIN, SHENGHONG LI, and SHILIN WANG. "AGGLOMERATIVE CLUSTERING BASED ON LABEL PROPAGATION FOR DETECTING OVERLAPPING AND HIERARCHICAL COMMUNITIES IN COMPLEX NETWORKS." Advances in Complex Systems 17, no. 06 (2014): 1450021. http://dx.doi.org/10.1142/s0219525914500210.

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Community detection is an important issue to understand the structural and functional properties of complex networks, which still remains a challenging subject. In some complex networks, a node may belong to multiple communities, implying overlapping community structure. Moreover, complex networks often show a hierarchical structure where small communities group together to form larger ones. In this paper, we propose a novel parameter-free algorithm called agglomerative clustering based on label propagation algorithm (ACLPA) to detect both overlapping and hierarchical community structure in co

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44

Zhang, Wei, Gongxuan Zhang, Yongli Wang, Zhaomeng Zhu, and Tao Li. "NBC: An Efficient Hierarchical Clustering Algorithm for Large Datasets." International Journal of Semantic Computing 09, no. 03 (2015): 307–31. http://dx.doi.org/10.1142/s1793351x15400085.

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Nearest neighbor search is a key technique used in hierarchical clustering and its computing complexity decides the performance of the hierarchical clustering algorithm. The time complexity of standard agglomerative hierarchical clustering is O(n3), while the time complexity of more advanced hierarchical clustering algorithms (such as nearest neighbor chain, SLINK and CLINK) is O(n2). This paper presents a new nearest neighbor search method called nearest neighbor boundary (NNB), which first divides a large dataset into independent subset and then finds nearest neighbor of each point in subset

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45

Firdaus, Firdaus. "Improving Data Integrity of Individual-based Bibliographic Repository Using Clustering Techniques." Computer Engineering and Applications Journal 7, no. 1 (2018): 49–56. http://dx.doi.org/10.18495/comengapp.v7i1.223.

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This paper presents a method to improve data integrity of individual-based bibliographic repository. Integrity improvement is done by comparing individual-based publication raw data with individual-based clustered publication data. Hierarchical Agglomerative Clustering is used to cluster the publication data with similar author names. Clustering is done by two steps of clustering. The first clustering is based on the co-author relationship and the second is by title similarity and year difference. The two-step hierarchical clustering technique for name disambiguation has been applied to Univer

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46

Hamasuna, Yukihiro, and Yasunori Endo. "Comparison of Semi-Supervised Hierarchical Clustering Using Clusterwise Tolerance." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 7 (2012): 819–24. http://dx.doi.org/10.20965/jaciii.2012.p0819.

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This paper presents a new semi-supervised agglomerative hierarchical clustering algorithm with the ward method using clusterwise tolerance. Semi-supervised clustering has recently been noted and studied in many research fields. Must-link and cannot-link, called pairwise constraints, are frequently used in order to improve clustering properties in semi-supervised clustering. First, clusterwise tolerance based pairwise constraints are introduced in order to handle mustlink and cannot-link constraints. Next, a new semisupervised hierarchical clustering algorithm with the ward method is constructe

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47

Wu, Shuangsheng, Jie Lin, Zhenyu Zhang, and Yushu Yang. "Hesitant Fuzzy Linguistic Agglomerative Hierarchical Clustering Algorithm and Its Application in Judicial Practice." Mathematics 9, no. 4 (2021): 370. http://dx.doi.org/10.3390/math9040370.

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The fuzzy clustering algorithm has become a research hotspot in many fields because of its better clustering effect and data expression ability. However, little research focuses on the clustering of hesitant fuzzy linguistic term sets (HFLTSs). To fill in the research gaps, we extend the data type of clustering to hesitant fuzzy linguistic information. A kind of hesitant fuzzy linguistic agglomerative hierarchical clustering algorithm is proposed. Furthermore, we propose a hesitant fuzzy linguistic Boole matrix clustering algorithm and compare the two clustering algorithms. The proposed cluste

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48

Fadliana, Alfi, and Fachrur Rozi. "Penerapan Metode Angglomerative Hierarchical Clustering untuk Klasifikasi Kabupaten/Kota di Propinsi Jawa Timur Berdasarkan Kualitas Pelayanan Keluarga Berencana." CAUCHY 4, no. 1 (2015): 25. http://dx.doi.org/10.18860/ca.v4i1.3172.

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Agglomerative hierarchical clustering methods is cluster analysis method whose primary purpose is to group objects based on its characteristics, it begins with the individual objects until the objects are fused into a single cluster. Agglomerative hierarchical clustering methods are divided into single linkage, complete linkage, average linkage, and ward. This research compared the four agglomerative hierarchical clustering methods in order to get the best cluster solution in the case of the classification of regencies/cities in East Java province based on the quality of “Keluarga Berencana” (

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49

Taleb, Tariq, and Mejdi Kaddour. "Hierarchical Agglomerative Clustering Schemes for Energy-Efficiency in Wireless Sensor Networks." Transport and Telecommunication Journal 18, no. 2 (2017): 128–38. http://dx.doi.org/10.1515/ttj-2017-0012.

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Abstract Extending the lifetime of wireless sensor networks (WSNs) while delivering the expected level of service remains a hot research topic. Clustering has been identified in the literature as one of the primary means to save communication energy. In this paper, we argue that hierarchical agglomerative clustering (HAC) provides a suitable foundation for designing highly energy efficient communication protocols for WSNs. To this end, we study a new mechanism for selecting cluster heads (CHs) based both on the physical location of the sensors and their residual energy. Furthermore, we study d

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50

Fujinami, Kaori. "UNSUPERVISED GROUPING OF MOVING OBJECTS BASED ON AGGLOMERATIVE HIERARCHICAL CLUSTERING." International Journal on Smart Sensing and Intelligent Systems 9, no. 4 (2016): 2276–96. http://dx.doi.org/10.21307/ijssis-2017-964.

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