Name
Abstract | ||
|---|---|---|
This paper presents two methods based on self-organized dissimilarity. The first is an implemented fuzzy clustering and the second is a hybrid method of fuzzy clustering and multidimensional scaling (MDS). Specifically, a self-organized dissimilarity is defined that uses the result of fuzzy clustering in such a way that the dissimilarity of objects is influenced by the dissimilarity of the classification situations corresponding to the objects. In other words, the dissimilarity is defined under an assumption that similar objects have similar classification structures. Through empirical evaluation the proportion and the fitness of the results of the method, which uses MDS combined with fuzzy clustering, is shown to be effective in real data. Furthermore, by exploiting the self-organized similarity, defuzzification of fuzzy clustering can cope with the inherent classification structures |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/FUZZY.2005.1452526 | Reno, NV |
Keywords | Field | DocType |
data analysis,fuzzy set theory,pattern clustering,self-organising feature maps,classification structures,defuzzification,fuzzy clustering,multidimensional scaling,object dissimilarity,self-organized dissimilarity,self-organized methods | Data mining,Fuzzy clustering,Fuzzy classification,Computer science,Fuzzy set,Artificial intelligence,FLAME clustering,Cluster analysis,Single-linkage clustering,Correlation clustering,Defuzzification,Pattern recognition,Machine learning | Conference |
ISBN | Citations | PageRank |
0-7803-9159-4 | 3 | 1.21 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mika Sato-Ilic | 1 | 32 | 16.09 |
| Tomoyuki Kuwata | 2 | 3 | 1.55 |
| Sato-Ilic, M. | 3 | 3 | 1.21 |