HCuRMD: Hierarchical Clustering Using Relative Minimal Distances

Abstract : In recent years, the ever increasing production of huge amounts of data has led the research community into trying to find new machine learning techniques in order to gain insight and discover hidden structures and correlation among these data. Therefore, clustering has become one of the most widely used techniques for exploratory data analysis. In this sense, this paper is proposing a new approach in hierarchical clustering; named HCuRMD, which improves the overall complexity of the whole clustering process by using a more relative perspective in defining minimal distances among different objects.
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Richard Chbeir; Yannis Manolopoulos; Ilias Maglogiannis; Reda Alhajj. 11th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2015), Sep 2015, Bayonne, France. IFIP Advances in Information and Communication Technology, AICT-458, pp.440-447, 2015, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-23868-5_32〉
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Charalampos Goulas, Dimitrios Chondrogiannis, Theodoros Xenakis, Alexandros Xenakis, Photis Nanopoulos. HCuRMD: Hierarchical Clustering Using Relative Minimal Distances. Richard Chbeir; Yannis Manolopoulos; Ilias Maglogiannis; Reda Alhajj. 11th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2015), Sep 2015, Bayonne, France. IFIP Advances in Information and Communication Technology, AICT-458, pp.440-447, 2015, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-23868-5_32〉. 〈hal-01385380〉

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