International audienceIn the data mining field many clustering methods have been proposed, yet standard versions do not take into account uncertain databases. This paper deals with a new approach to cluster uncertain data by using a hierarchical clustering defined within the belief function framework. The main objective of the belief hierarchical clustering is to allow an object to belong to one or several clusters. To each belonging, a degree of belief is associated, and clusters are combined based on the pignistic properties. Experiments with real uncertain data show that our proposed method can be considered as a propitious tool
We study how to derive a fuzzy rule-based classification model using the theoretical framework of be...
AbstractA new online clustering method called E2GK (Evidential Evolving Gustafson–Kessel) is introdu...
International audienceCommunity detection is a popular topic in network science field. In social net...
International audienceIn the data mining field many clustering methods have been proposed, yet stand...
International audienceThis paper introduces a new evidential clustering method based on the notion o...
International audienceThe well-known Fuzzy C-Means (FCM) algorithm for data clustering has been exte...
Evidential clustering based on the theory of belief functions has become one of the topics of machin...
International audienceThis paper reports on an investigation in classification technique employed to...
International audienceIn this paper, we propose a clustering ensemble method based on Dempster-Shafe...
In this paper, belief functions, defined on the lattice of intervals partitions of a set of objects,...
This paper targets the problem of computing meaningful clusterings from uncertain data sets. Existin...
In this paper we present a method to detect natural groups in a data set, based on hierarchical clus...
The objective of data mining is to take out information from large amounts of data and convert it in...
Fifty years have gone by since the publication of the first paper on clustering based on fuzzy sets ...
This paper targets the problem of computing meaningful clusterings from uncertain data sets. Existin...
We study how to derive a fuzzy rule-based classification model using the theoretical framework of be...
AbstractA new online clustering method called E2GK (Evidential Evolving Gustafson–Kessel) is introdu...
International audienceCommunity detection is a popular topic in network science field. In social net...
International audienceIn the data mining field many clustering methods have been proposed, yet stand...
International audienceThis paper introduces a new evidential clustering method based on the notion o...
International audienceThe well-known Fuzzy C-Means (FCM) algorithm for data clustering has been exte...
Evidential clustering based on the theory of belief functions has become one of the topics of machin...
International audienceThis paper reports on an investigation in classification technique employed to...
International audienceIn this paper, we propose a clustering ensemble method based on Dempster-Shafe...
In this paper, belief functions, defined on the lattice of intervals partitions of a set of objects,...
This paper targets the problem of computing meaningful clusterings from uncertain data sets. Existin...
In this paper we present a method to detect natural groups in a data set, based on hierarchical clus...
The objective of data mining is to take out information from large amounts of data and convert it in...
Fifty years have gone by since the publication of the first paper on clustering based on fuzzy sets ...
This paper targets the problem of computing meaningful clusterings from uncertain data sets. Existin...
We study how to derive a fuzzy rule-based classification model using the theoretical framework of be...
AbstractA new online clustering method called E2GK (Evidential Evolving Gustafson–Kessel) is introdu...
International audienceCommunity detection is a popular topic in network science field. In social net...