DocumentCode :
2456101
Title :
Map-TreeMaps: A New Approach for Hierarchical and Topological Clustering
Author :
Azzag, Hanene ; Lebbah, Mustapha ; Arfaoui, Aymen
Author_Institution :
Dept. of Comput. Sci., Univ. of Paris 13, Villetaneuse, France
fYear :
2010
fDate :
12-14 Dec. 2010
Firstpage :
873
Lastpage :
878
Abstract :
We present in this paper a new clustering method which provides self-organization of hierarchical clustering. This method represents large datasets on a forest of original trees which are projected on a simple 2D geometric relationship using tree map representation. The obtained partition is represented by a map of tree maps, which define a tree of data. In this paper, we provide the rules that build a tree of node/data by using distance between data in order to decide where connect nodes. Visual and empirical results based on both synthetic and real datasets from the UCI repository, are given and discussed.
Keywords :
data visualisation; geometry; pattern clustering; self-organising feature maps; 2D geometric relationship; hierarchical clustering; topological clustering; tree map representation; Clustering algorithms; Cost function; Data visualization; Indexes; Numerical models; Organizations; Visualization; Hierarchical clustering; Self-Organizing-Map; Treemaps; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4244-9211-4
Type :
conf
DOI :
10.1109/ICMLA.2010.136
Filename :
5708959
Link To Document :
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