Towards Hierarchical Clustering (Extended Abstract)
作者： Mark Sh. Levin
DOI: 10.1007/978-3-540-74510-5_22
关键词: Data mining 、 Correlation clustering 、 Cluster analysis 、 Artificial intelligence 、 Machine learning 、 Hierarchical clustering of networks 、 Conceptual clustering 、 Single-linkage clustering 、 Brown clustering 、 Computer science 、 Hierarchical clustering 、 Consensus clustering
摘要: In the paper, new modified agglomerative algorithms for hierarchical clustering are suggested. The process is targeted to generating a cluster hierarchy which can contain same items in different clusters. based on following additional operations: (i) building an ordinal item pair proximity (’distance’) including usage of multicriteria approaches; (ii) integration several at each stage algorithms; and (iii) inclusion into integrated pairs/clusters. suggested modifications above significant from viewpoints practice, e.g., design systems architecture engineering computer systems.
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