A successful attempt in exploring a dissimilarity measure which captures the reality is made in this paper. The proposed measure unlike other measures (Pattern Recognition 24(6) (1991) 567; Pattern Recognition Lett. 16 (1995) 647; Pattern Recognition 28(8) (1995) 1277; IEEE Trans. Syst. Man Cybern. 24(4) (1994)) is multivalued and non-symmetric. The concept of mutual dissimilarity value is introduced to make the existing conventional clustering algorithms work on the proposed unconventional dissimilarity measure
The aim of this paper consists in showing the building of dissimilarity measures between either Bool...
Abstract We deal with methods for analyzing complex structured data, especially, distribution valued...
One of the critical aspects of clustering algorithms is the correct identification of the dissimilar...
In this paper, a novel similarity measure for estimating the degree of similarity between two patter...
We propose a novel hierarchical clustering for distribution valued dissimilarities. Analysis of larg...
Clustering is the process of grouping a set ofphysical or abstract objects into classes of similarob...
In this paper, a novel similarity measure for estimating the degree of similarity between two symbol...
This paper presents an approach to calculate the dissimilarity between probabilistic symbolic object...
This paper presents an approach to calculate the dissimilarity between probabilistic symbolic object...
In this paper we propose a new index Z for measuring the dissimilaritybetween two hierarchical clust...
In this paper we propose a new index Z for measuring the dissimilarity between two hierarchical clus...
A nonparametric, hierarchical, disaggregative clustering algorithm is developed using a novel simila...
International audienceWe present a new algorithm capable of partitioning sets of objects by taking s...
The cosine or correlation measures of similarity used to cluster high dimensional data are interpret...
In this paper, we study the notion of entropy for a set of attributes of a table and propose a novel...
The aim of this paper consists in showing the building of dissimilarity measures between either Bool...
Abstract We deal with methods for analyzing complex structured data, especially, distribution valued...
One of the critical aspects of clustering algorithms is the correct identification of the dissimilar...
In this paper, a novel similarity measure for estimating the degree of similarity between two patter...
We propose a novel hierarchical clustering for distribution valued dissimilarities. Analysis of larg...
Clustering is the process of grouping a set ofphysical or abstract objects into classes of similarob...
In this paper, a novel similarity measure for estimating the degree of similarity between two symbol...
This paper presents an approach to calculate the dissimilarity between probabilistic symbolic object...
This paper presents an approach to calculate the dissimilarity between probabilistic symbolic object...
In this paper we propose a new index Z for measuring the dissimilaritybetween two hierarchical clust...
In this paper we propose a new index Z for measuring the dissimilarity between two hierarchical clus...
A nonparametric, hierarchical, disaggregative clustering algorithm is developed using a novel simila...
International audienceWe present a new algorithm capable of partitioning sets of objects by taking s...
The cosine or correlation measures of similarity used to cluster high dimensional data are interpret...
In this paper, we study the notion of entropy for a set of attributes of a table and propose a novel...
The aim of this paper consists in showing the building of dissimilarity measures between either Bool...
Abstract We deal with methods for analyzing complex structured data, especially, distribution valued...
One of the critical aspects of clustering algorithms is the correct identification of the dissimilar...