The main contribution of this paper is to show how to apply the concept of fuzziness on a data set of symbolic objects and how to use this concept in ...
Fuzzy clustering generates a fuzzy partition based on the idea of partial membership expressed by the degree of membership of each pattern in a given cluster.
Most of the techniques used in the literature in clustering symbolic data are based on the hierarchical methodology, which utilizes the concept of ...
Abstract—Most of the techniques used in the literature in clus- tering symbolic data are based on the hierarchical methodology,.
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A fuzzy symbolic c-means algorithm is introduced as an application of applying and testing the proposed algorithm on real and synthetic data sets and the ...
This paper introduces fuzzy clustering algorithms to partitioning symbolic interval data. The proposed methods furnish a fuzzy partition and a prototype (a ...
Mar 1, 2007 · This paper presents adaptive and non-adaptive fuzzy c-means clustering methods for partitioning symbolic interval data.
The recording of symbolic interval data has become a common practice with the recent advances in database technologies. This paper presents a fuzzy c-means ...
This paper presents adaptive and non-adaptive fuzzy c-means clustering methods for partitioning symbolic interval data. The pro- posed methods furnish a fuzzy ...
The main contribution of this paper is to show how to apply the concept of fuzziness on a data set of symbolic objects and how to use this concept in ...