Relevant bibliographies by topics / Divisive hierarchical clustering

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Divisive hierarchical clustering'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Divisive hierarchical clustering"

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Anagnostou, Panagiotis, Sotiris Tasoulis, Vassilis P. Plagianakos, and Dimitris Tasoulis. "HiPart: Hierarchical Divisive Clustering Toolbox." Journal of Open Source Software 8, no. 84 (2023): 5024. http://dx.doi.org/10.21105/joss.05024.

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Simion, Ileana M., Augustin-C. Moţ, and Costel Sârbu. "Finding specific peaks (markers) using fuzzy divisive hierarchical associative-clustering based on the chromatographic profiles of medicinal plant extracts obtained at various detection wavelengths." Analytical Methods 12, no. 25 (2020): 3260–67. http://dx.doi.org/10.1039/d0ay00295j.

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Advanced chemometric methods, such as fuzzy c-means (FCM), a fuzzy divisive hierarchical clustering algorithm (FDHC), and fuzzy divisive hierarchical associative-clustering (FDHAC), have been successfully applied in this study.
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Nietto, Paulo Rogério, and Maria Do Carmo Nicoletti. "Case studies in divisive hierarchical clustering." International Journal of Innovative Computing and Applications 8, no. 2 (2017): 102. http://dx.doi.org/10.1504/ijica.2017.084893.

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Nietto, Paulo Rogério, and Maria Do Carmo Nicoletti. "Case studies in divisive hierarchical clustering." International Journal of Innovative Computing and Applications 8, no. 2 (2017): 102. http://dx.doi.org/10.1504/ijica.2017.10005945.

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Xiong, Tengke, Shengrui Wang, André Mayers, and Ernest Monga. "DHCC: Divisive hierarchical clustering of categorical data." Data Mining and Knowledge Discovery 24, no. 1 (2011): 103–35. http://dx.doi.org/10.1007/s10618-011-0221-2.

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Harahap, Ahir Yugo Nugroho, Ria Eka Sari, Heri Gunawan, and Adnan Buyung Nasution. "Evaluation of Product Sales Data Using Clustering Method and Hierarchical Divisive Clustering at PT.AYN." Indonesian Journal of Interdisciplinary Research in Science and Technology 2, no. 7 (2024): 1145–58. http://dx.doi.org/10.55927/marcopolo.v2i7.10442.

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Data Mining, focusing on the Hierarchical Divisive algorithm, can provide a solution for PT.AYN in overcoming the problem of unifying and evaluating sales data, a company that sells various types of disposable tissues. This study aims to identify products that are in demand and less in demand and to group sales data based on company and product type. The results of this study provide valuable insights for evaluating sales data, understanding distributor purchasing trends, and supporting more effective stock planning, shipping, and marketing strategies. Through the application of the clustering
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Chen, Lin Chih. "Building a Post-Search Academic Search Engine Based on a Serial of Clustering Methods." Applied Mechanics and Materials 284-287 (January 2013): 3051–55. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3051.

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Academic search engines, such as Google Scholar and Scirus, provide a Web-based interface to effectively find relevant scientific articles to researchers. However, current academic search engines are lacking the ability to cluster the search results into a hierarchical tree structure. In this paper, we develop a post-search academic search engine by using a mixed clustering method. In this method, we first adopt a suffix tree clustering and a two-way hash mechanism to generate all meaningful labels. We then develop a divisive hierarchical clustering algorithm to organize the labels into a hier
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Kamat, Prof Vaishnavi, Prof Terence Johnson, Rudresh Chodankar, Rama Harmalkar, Gauresh Naik, and Prajyot Narulkar. "Document Clustering Using Divisive Hierarchical Bisecting Min Max Clustering Algorithm." IOSR Journal of Computer Engineering 19, no. 03 (2017): 66–70. http://dx.doi.org/10.9790/0661-1903066670.

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Chavent, Marie, Yves Lechevallier, and Olivier Briant. "DIVCLUS-T: A monothetic divisive hierarchical clustering method." Computational Statistics & Data Analysis 52, no. 2 (2007): 687–701. http://dx.doi.org/10.1016/j.csda.2007.03.013.

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Luo, Junhai, Lei Ye, and Xiaoting He. "Neighbors-based divisive algorithm for hierarchical analysis in networks." International Journal of Modern Physics C 30, no. 07 (2019): 1940003. http://dx.doi.org/10.1142/s0129183119400035.

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Hierarchical analysis for network structure can point out which communities can constitute a larger group or give reasonable smaller groups within a community. Numerous methods for discovering community in networks divide networks at only one certain granularity, which does not benefit hierarchical analysis for network structure. Hierarchical clustering algorithms are the common technique that reveals the multilevel structure in the network analysis. In this work, we give a definition for scores of edges according to the basic idea of means clustering. Based on the definition, a neighbors-base
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Dissertations / Theses on the topic "Divisive hierarchical clustering"

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Grek, Viktoria, and Molinia Gabrielsson. "Analys av nutidens tågindelning : Ett uppdrag framtaget av Trafikverket." Thesis, Linköpings universitet, Statistik och maskininlärning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150472.

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The information used in this paper comes from Trafikverket's delivery monitoring system. It consists of information about planned train missions on the Swedish railways for the years 2014 to 2017 during week four (except planned train missions on Roslagsbanan and Saltsjöbanan). Trafikanalys with help from Trafikverket presents public statistics for short-distance trains, middle-distance trains and long-distance trains on Trafikanalys website. The three classes of trains have no scientific basis. The purpose of this study is therefore to analyze if today's classes of trains can be used and whic
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Book chapters on the topic "Divisive hierarchical clustering"

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Gan, Guojun. "A Divisive Hierarchical Clustering Algorithm." In Data Clustering with Python. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003592648-7.

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Amalaman, Paul K., and Christoph F. Eick. "Avalanche: A Hierarchical, Divisive Clustering Algorithm." In Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21024-7_20.

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Terence Johnson and Santosh Kumar Singh. "Divisive Hierarchical Bisecting Min–Max Clustering Algorithm." In Proceedings of the International Conference on Data Engineering and Communication Technology. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1675-2_57.

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Jayaprada, S., Amarapini Aswani, and G. Gayathri. "Hierarchical Divisive Clustering with Multi View-Point Based Similarity Measure." In Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02931-3_55.

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Legát, Milan, and Jindřich Matoušek. "Identifying Concatenation Discontinuities by Hierarchical Divisive Clustering of Pitch Contours." In Text, Speech and Dialogue. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23538-2_22.

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Xiong, Tengke, Shengrui Wang, André Mayers, and Ernest Monga. "Semi-supervised Parameter-Free Divisive Hierarchical Clustering of Categorical Data." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20841-6_22.

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Musa, Ibrahim Musa Ishag, Anour F. A. Dafa-Alla, Gyeong Min Yi, et al. "Online Multi-divisive Hierarchical Clustering for On-Body Sensor Data." In Advances in Experimental Medicine and Biology. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5913-3_10.

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Ievlanov, Maksym, Nataliya Vasiltcova, Olga Neumyvakina, and Iryna Panforova. "Use of clustering methods to solve the problem of identifying configuration items in IT project." In PROJECT MANAGEMENT: INDUSTRY SPECIFICS. TECHNOLOGY CENTER PC, 2024. https://doi.org/10.15587/978-617-8360-03-0.ch1.

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The object of the research is the IT project configuration management process. During the research, the task of identifying the configuration items (CI) of the IT product has been solved. Research in this field is mainly aimed at solving the problem of configuration analysis during the refactoring of a monolithic IT product into separate services or microservices. The question of decomposition methods of the description of the architecture of the developed IT product into separate functional SIs remains practically unexplored. In the course of the research, it has been proposed to use hierarch
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Wang, Xiaochun, Xiali Wang, and Mitch Wilkes. "A Fast Distance-Based Outlier Detection Technique Using a Divisive Hierarchical Clustering Algorithm." In New Developments in Unsupervised Outlier Detection. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9519-6_3.

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Liu, Jian-Wei, Shou-Jian Yu, and Jia-Jin Le. "Dynamic Mining Hierarchical Topic from Web News Stream Data Using Divisive-Agglomerative Clustering Method." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11430919_97.

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Conference papers on the topic "Divisive hierarchical clustering"

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Yuruk, Nurcan, Mutlu Mete, Xiaowei Xu, and Thomas A. J. Schweiger. "A Divisive Hierarchical Structural Clustering Algorithm for Networks." In 2007 Seventh IEEE International Conference on Data Mining - Workshops (ICDM Workshops). IEEE, 2007. http://dx.doi.org/10.1109/icdmw.2007.73.

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Yamada, Yuna, Naoki Masuyama, Narito Amako, Yusuke Nojima, Chu Kiong Loo, and Hisao Ishibuchi. "Divisive Hierarchical Clustering Based on Adaptive Resonance Theory." In 2020 International Symposium on Community-centric Systems (CcS). IEEE, 2020. http://dx.doi.org/10.1109/ccs49175.2020.9231474.

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De Smet, Y. "An extension of PROMETHEE to divisive hierarchical multicriteria clustering." In 2014 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2014. http://dx.doi.org/10.1109/ieem.2014.7058699.

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Xiong, Tengke, Shengrui Wang, André Mayers, and Ernest Monga. "A New MCA-Based Divisive Hierarchical Algorithm for Clustering Categorical Data." In 2009 Ninth IEEE International Conference on Data Mining (ICDM). IEEE, 2009. http://dx.doi.org/10.1109/icdm.2009.118.

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Bordogna, Gloria, and Gabriella Pasi. "Hierarchical-Hyperspherical Divisive Fuzzy C-Means (H2D-FCM) Clustering for Information Retrieval." In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2009. http://dx.doi.org/10.1109/wi-iat.2009.104.

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Kokturk, Basak Esin, and Bilge Karacali. "Model-free expectation maximization for divisive hierarchical clustering of multicolor flow cytometry data." In 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2014. http://dx.doi.org/10.1109/bibm.2014.6999166.

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Ahn, Hongryul, Inuk Jung, Heejoon Chae, Minsik Oh, Inyoung Kim, and Sun Kim. "IDEA: Integrating Divisive and Ensemble-Agglomerate hierarchical clustering framework for arbitrary shape data." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671953.

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Chan, Tian, Jürgen Mihm, and Manuel Sosa. "A Structured Approach to Identify Styles in Designs." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70717.

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This paper introduces a structured approach, based on clustering analysis, to identify groups of visually similar designs (and potentially members of a design style) in a large set of unique designs. We implemented our approach on the population of design patents filed in the USA during the 1976–2010 period. Design patent data is used in our analysis for two reasons: (1) claims of design patents concern the visual aspects of classes of artifacts; (2) similarity between design patents is captured through a tightly controlled citation process. The cluster analysis is based on constructing a simi
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Wu, Yulang, George A. McMechan, and Yanfei Wang. "A spatially constrained divisive hierarchical k-means clustering to capture prior features from migration velocity model to build training model set for deep-learning LSRTM." In First International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists, 2021. http://dx.doi.org/10.1190/segam2021-3581550.1.

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Reports on the topic "Divisive hierarchical clustering"

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Berry, Jessica, and Thomas Saller. Hierarchical Division and Clustering of Group Structures. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1888179.

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