Title :
Clustering objects with degree of classification
Author_Institution :
Sch. of Syst. & Inf. Eng., Univ. of Tsukuba, Tsukuba
Abstract :
This paper proposes a fuzzy clustering method under the intrinsically classified structure of data through dissimilarity of objects at each variable. In order to extract the classification structure, the variable-based fuzzy clustering method is exploited and the degree of classification for each object with respect to each variable is defined. This degree shows individually classified power of an object with respect to a variable. By applying this degree to the data, a stable classification solution which is not sensitive to the outlier is obtained. Several numerical examples show the improved performance and the applicability of our proposed method.
Keywords :
fuzzy set theory; pattern classification; pattern clustering; classification structure extraction; fuzzy clustering method; object clustering; Clustering algorithms; Clustering methods; Data analysis; Data mining; Equations; Euclidean distance; Information analysis; Information technology; Input variables; Stability;
Conference_Titel :
Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1818-3
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2008.4630605