Title :
Relational Gustafson Kessel Clustering Using Medoids and Triangulation
Author :
Runkler, Thomas A.
Author_Institution :
Dept. of Neural Comput., Siemens AG, Munich
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;
Conference_Titel :
Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on
Conference_Location :
Reno, NV
Print_ISBN :
0-7803-9159-4
DOI :
10.1109/FUZZY.2005.1452371