DocumentCode
3398852
Title
Relational Gustafson Kessel Clustering Using Medoids and Triangulation
Author
Runkler, Thomas A.
Author_Institution
Dept. of Neural Comput., Siemens AG, Munich
fYear
2005
fDate
25-25 May 2005
Firstpage
73
Lastpage
78
Abstract
This paper deals with clustering relational data that can be (at least approximately) represented by object data with ellipsoidal clusters. Conventional relational clustering models such as relational fuzzy c-means or relational fuzzy c-medoids produce bad results for this family of relational data, because they do not consider the cluster shape. In this paper, we develop a Gustafson Kessel model where the cluster centers are medoids. For relational data, the scatter matrices and the matrix distances are locally computed using triangulation. The resulting RGKMdd algorithm produces very good results for the family of relational data specified above
Keywords
fuzzy set theory; pattern clustering; relational databases; Gustafson-Kessel clustering; ellipsoidal clusters; matrix distances; relational data clustering; relational fuzzy c-means; relational fuzzy c-medoids; scatter matrix; triangulation; Clustering algorithms; Communications technology; Computational complexity; Fuzzy sets; Fuzzy systems; Iterative algorithms; Scattering; Shape;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on
Conference_Location
Reno, NV
Print_ISBN
0-7803-9159-4
Type
conf
DOI
10.1109/FUZZY.2005.1452371
Filename
1452371
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