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  • The main aim of the work was to apply some results of fuzzy sets theory and to develop new elements of hierarchical agglomerative clustering alforithms especially focused on manipulating with fuzzy data. The main goal consisted of the following four parts: ? to prepare the fuzzy data for cluster analysis; ? to extend the definition of object dissimilarity for the case of fuzzy objects; ? to realize clustering of fuzzy data; ? to search of Tolerance coefficient for Definite hierarchical agglomerative clustering method; ? to find Partition optimality coefficient by means of fuzzy c-means algorithm. Created algorithms were programmed and empirically verified on the model data and further used for research processing.
  • The main aim of the work was to apply some results of fuzzy sets theory and to develop new elements of hierarchical agglomerative clustering alforithms especially focused on manipulating with fuzzy data. The main goal consisted of the following four parts: ? to prepare the fuzzy data for cluster analysis; ? to extend the definition of object dissimilarity for the case of fuzzy objects; ? to realize clustering of fuzzy data; ? to search of Tolerance coefficient for Definite hierarchical agglomerative clustering method; ? to find Partition optimality coefficient by means of fuzzy c-means algorithm. Created algorithms were programmed and empirically verified on the model data and further used for research processing. (en)
Title
  • Fuzzy Approach to Hierarchical Clustering
  • Fuzzy Approach to Hierarchical Clustering (en)
skos:prefLabel
  • Fuzzy Approach to Hierarchical Clustering
  • Fuzzy Approach to Hierarchical Clustering (en)
skos:notation
  • RIV/61988987:17450/11:A11010HK!RIV11-GA0-17450___
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  • P(GA406/09/0242)
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  • 200690
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  • RIV/61988987:17450/11:A11010HK
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  • hiererchical clustering; fuzzy (en)
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  • [A0C405522C10]
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  • Fuzzy Approach to Hierarchical Clustering
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  • Malčík, Martin
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  • LAP Lambert Academic Publishing
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  • 978-3-8433-8864-1
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  • 17450
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