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On Agglomerative Hierarchical Clustering Using Clusterwise Tolerance Based Pairwise Constraints
Author(s) -
Yukihiro Hamasuna,
Yasunori Endo,
Sadaaki Miyamoto
Publication year - 2012
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2012.p0174
Subject(s) - pairwise comparison , hierarchical clustering , cluster analysis , computer science , hierarchical clustering of networks , data mining , brown clustering , single linkage clustering , artificial intelligence , correlation clustering , canopy clustering algorithm
This paper presents semi-supervised agglomerative hierarchical clustering algorithm using clusterwise tolerance based pairwise constraints. In semi-supervised clustering, pairwise constraints, that is, must-link and cannot-link, are frequently used in order to improve clustering properties. From that sense, we will propose another way named clusterwise tolerance based pairwise constraints to handle must-link and cannot-link constraints in L 2 -space. In addition, we will propose semi-supervised agglomerative hierarchical clustering algorithm based on it. We will, moreover, show the effectiveness of the proposed method through numerical examples.

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