Relevant bibliographies by topics / Hierarchical agglomerative clustering

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  2. Dissertations / Theses
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Academic literature on the topic 'Hierarchical agglomerative clustering'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Hierarchical agglomerative clustering"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Hierarchical agglomerative clustering"

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Cobo, Rodríguez Germán. "Parameter-free agglomerative hierarchical clustering to model learners' activity in online discussion forums." Doctoral thesis, Universitat Oberta de Catalunya, 2014. http://hdl.handle.net/10803/133926.

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L'anàlisi de l'activitat dels estudiants en els fòrums de discussió online implica un problema de modelització altament depenent del context, el qual pot ser plantejat des d'aproximacions tant teòriques com empíriques. Quan aquest problema és abordat des de l'àmbit de la mineria de dades, l'enfocament més comunament adoptat és el de la classificació no supervisada (o clustering), donant lloc, d'aquesta manera, a un escenari de clustering en el qual el nombre real de clústers és a priori desconegut. Per tant, aquesta aproximació revela una qüestió subjacent, la qual no és sinó un dels problemes
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Li, Na. "MMD and Ward criterion in a RKHS : application to Kernel based hierarchical agglomerative clustering." Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0033/document.

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La classification non supervisée consiste à regrouper des objets afin de former des groupes homogènes au sens d’une mesure de similitude. C’est un outil utile pour explorer la structure d’un ensemble de données non étiquetées. Par ailleurs, les méthodes à noyau, introduites initialement dans le cadre supervisé, ont démontré leur intérêt par leur capacité à réaliser des traitements non linéaires des données en limitant la complexité algorithmique. En effet, elles permettent de transformer un problème non linéaire en un problème linéaire dans un espace de plus grande dimension. Dans ce travail,
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Hobro, Mark. "Semantic Integration across Heterogeneous Databases : Finding Data Correspondences using Agglomerative Hierarchical Clustering and Artificial Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-226657.

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The process of data integration is an important part of the database field when it comes to database migrations and the merging of data. The research in the area has grown with the addition of machine learning approaches in the last 20 years. Due to the complexity of the research field, no go-to solutions have appeared. Instead, a wide variety of ways of enhancing database migrations have emerged. This thesis examines how well a learning-based solution performs for the semantic integration problem in database migrations. Two algorithms are implemented. One that is based on information retrieva
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Hinz, Joel. "Clustering the Web : Comparing Clustering Methods in Swedish." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-95228.

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Clustering -- automatically sorting -- web search results has been the focus of much attention but is by no means a solved problem, and there is little previous work in Swedish. This thesis studies the performance of three clustering algorithms -- k-means, agglomerative hierarchical clustering, and bisecting k-means -- on a total of 32 corpora, as well as whether clustering web search previews, called snippets, instead of full texts can achieve reasonably decent results. Four internal evaluation metrics are used to assess the data. Results indicate that k-means performs worse than the other tw
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Crossman, Nathaniel C. "Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1590957641168863.

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MOURA, Eduardo Santiago. "Agrupamento de faces em vídeos digitais." Universidade Federal de Campina Grande, 2016. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/894.

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Submitted by Maria Medeiros (maria.dilva1@ufcg.edu.br) on 2018-06-06T11:40:34Z No. of bitstreams: 1 EDUARDO SANTIAGO MOURA - TESE (PPGCC) 2016.pdf: 4888830 bytes, checksum: b0fd54b306e9a1dfeb9e68ce43716fa2 (MD5)<br>Made available in DSpace on 2018-06-06T11:40:34Z (GMT). No. of bitstreams: 1 EDUARDO SANTIAGO MOURA - TESE (PPGCC) 2016.pdf: 4888830 bytes, checksum: b0fd54b306e9a1dfeb9e68ce43716fa2 (MD5) Previous issue date: 2016<br>Faces humanas são algumas das entidades mais importantes frequentemente encontradas em vídeos. Devido ao substancial volume de produção e consumo de vídeos digitais
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Bahirwani, Vishal. "Exploring transcription patterns and regulatory motifs in Arabidopsis thaliana." Thesis, Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/4194.

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Pospíšil, David. "Shluková analýza signálu EKG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-219954.

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This diploma thesis deals with the use of some methods of cluster analysis on the ECG signal in order to sort QRS complexes according to their morphology to normal and abnormal. It is used agglomerative hierarchical clustering and non-hierarchical method K – Means for which an application in Mathworks MATLAB programming equipment was developed. The first part deals with the theory of the ECG signal and cluster analysis, and then the second is the design, implementation and evaluation of the results of the usage of developed software on the ECG signal for the automatic division of QRS complexes
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Liao, Chiu-Min, and 廖秋閔. "Agglomerative Hierarchical clustering with the string data." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/82370452167764942267.

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碩士<br>國立中央大學<br>工業管理研究所<br>102<br>Due to the progressing of the science and technology, the data is growing rapidly. Data mining help us to organize the thousands of data efficiently and the managers can obviously find out the information that they do not know before and make appropriate decisions. Cluster analysis is one of the methods that are widely used in data mining according to the features of the data. Most of data applied to cluster analysis are qualitative and quantitative and the string data (flow data) is seldom discussed in cluster analysis. Therefore in this research, we try to p
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Lin, Tsung-Hau, and 林琮晧. "An extension of Fuzzy Agglomerative Hierarchical Clustering." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/41036400665248006823.

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碩士<br>國立中興大學<br>資訊管理學系所<br>104<br>In traditional clustering algorithms, an instance will only belong to a cluster, but in real world such as biology, text mining and news media often appear instance that belongs to more than one clusters at the same time. This phenomenon shows that clustering algorithms should not only consider the instance that exists in disjoint cluster but also the overlapping instance between clusters. In this paper, we present an overlapping clustering algorithm based on Fuzzy Agglomerative Hierarchical Clustering (FAHC) by using hard memberships clustering method. FAHC w
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Books on the topic "Hierarchical agglomerative clustering"

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Voorhees, E. The effectiveness and efficiency of agglomerative hierarchic clustering in document retrieval. 1989.

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The effectiveness and efficiency of agglomerative hierarchic clustering in document retrieval. University Microfilms International, 1986.

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Book chapters on the topic "Hierarchical agglomerative clustering"

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Zepeda-Mendoza, Marie Lisandra, and Osbaldo Resendis-Antonio. "Hierarchical Agglomerative Clustering." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1371.

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García-Lapresta, José Luis, and David Pérez-Román. "Consensus-Based Agglomerative Hierarchical Clustering." In Fuzzy Sets, Rough Sets, Multisets and Clustering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47557-8_8.

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Fujiwara, Yuji, and Hisashi Koga. "Multiviewpoint-Based Agglomerative Hierarchical Clustering." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27618-8_24.

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Miyamoto, Sadaaki. "Refinement Properties in Agglomerative Hierarchical Clustering." In Modeling Decisions for Artificial Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04820-3_24.

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Chen, Danny Z., and Bin Xu. "Geometric Algorithms for Agglomerative Hierarchical Clustering." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45071-8_5.

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Fillbrunn, Alexander, and Michael R. Berthold. "Diversity-Driven Widening of Hierarchical Agglomerative Clustering." In Advances in Intelligent Data Analysis XIV. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24465-5_8.

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Angeletti, Mélodie, Jean-Marie Bonny, Franck Durif, and Jonas Koko. "Parallel Hierarchical Agglomerative Clustering for fMRI Data." In Parallel Processing and Applied Mathematics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78024-5_24.

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Froud, Hanane, and Abdelmonaime Lachkar. "Agglomerative Hierarchical Clustering Techniques for Arabic Documents." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00951-3_25.

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Batagelj, V., and A. Ferligoj. "Agglomerative Hierarchical Multicriteria Clustering Using Decision Rules." In Compstat. Physica-Verlag HD, 1990. http://dx.doi.org/10.1007/978-3-642-50096-1_3.

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Sheikhalishahi, Mina, Mona Hamidi, and Fabio Martinelli. "Privacy Preserving Collaborative Agglomerative Hierarchical Clustering Construction." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25109-3_14.

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Conference papers on the topic "Hierarchical agglomerative clustering"

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Monath, Nicholas, Kumar Avinava Dubey, Guru Guruganesh, et al. "Scalable Hierarchical Agglomerative Clustering." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2021. http://dx.doi.org/10.1145/3447548.3467404.

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Durante, Fabrizio, Aurora Gatto, and Susanne Saminger-Platz. "On Agglomerative Hierarchical Percentile Clustering." In 19th World Congress of the International Fuzzy Systems Association (IFSA), 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and 11th International Summer School on Aggregation Operators (AGOP). Atlantis Press, 2021. http://dx.doi.org/10.2991/asum.k.210827.083.

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Haifeng Zhao and Zijie Qi. "Hierarchical Agglomerative Clustering with Ordering Constraints." In 2010 3rd International Conference on Knowledge Discovery and Data Mining (WKDD 2010). IEEE, 2010. http://dx.doi.org/10.1109/wkdd.2010.123.

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Mall, Raghvendra, Rocco Langone, and Johan A. K. Suykens. "Agglomerative hierarchical kernel spectral data clustering." In 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, 2014. http://dx.doi.org/10.1109/cidm.2014.7008142.

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Hung, Phan Duy, Nguyen Thi Thuy Lien, and Nguyen Duc Ngoc. "Customer Segmentation Using Hierarchical Agglomerative Clustering." In ICISS 2019: 2019 the 2nd International Conference on Information Science and Systems. ACM, 2019. http://dx.doi.org/10.1145/3322645.3322677.

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Makrehchi, Masoud. "Hierarchical Agglomerative Clustering Using Common Neighbours Similarity." In 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI). IEEE, 2016. http://dx.doi.org/10.1109/wi.2016.0093.

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Yasunori, Endo, Hamasuna Yukihiro, and Miyamoto Sadaaki. "Agglomerative Hierarchical Clustering for Data with Tolerance." In 2007 IEEE International Conference on Granular Computing. IEEE, 2007. http://dx.doi.org/10.1109/grc.2007.107.

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Endo, Yasunori, Yukihiro Hamasuna, and Sadaaki Miyamoto. "Agglomerative Hierarchical Clustering for Data with Tolerance." In 2007 IEEE International Conference on Granular Computing (GRC 2007). IEEE, 2007. http://dx.doi.org/10.1109/grc.2007.4403132.

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Daniels, Kristine, and Christophe Giraud-Carrier. "Learning the Threshold in Hierarchical Agglomerative Clustering." In 2006 5th International Conference on Machine Learning and Applications (ICMLA'06). IEEE, 2006. http://dx.doi.org/10.1109/icmla.2006.33.

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Wang, Dajun, Paul Fortier, Howard Michel, and Theophano Mitsa. "Hierarchical Agglomerative Clustering Based T-outlier Detection." In Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06). IEEE, 2006. http://dx.doi.org/10.1109/icdmw.2006.91.

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