Clustering techniques are considered as efficient tools for partitioning data sets in order to ge... more Clustering techniques are considered as efficient tools for partitioning data sets in order to get homogeneous clusters of objects. However, the reality is connected to uncertainty by nature, and these standard algorithms of clustering do not deal with this uncertainty pervaded in their parameters. In this paper we develop a clustering method in an uncertain context based on the K-modes
ABSTRACT In this paper, we investigate the problem of dynamic belief clustering. The developed ap... more ABSTRACT In this paper, we investigate the problem of dynamic belief clustering. The developed approach tackles the problem of updating the partition by decreasing the attribute set in an uncertain context. We propose a based-ranking feature selection method that allows us to preserve only the most relevant attributes. We deal with uncertainty related to attribute values, which is represented and managed through the Transferable Belief Model (TBM) concepts. The reported results showed that, in general, there is a beneficial effect of using the developed selection method to cluster dynamic feature set in comparison with the other static methods performing a complete reclustering.
ACS/IEEE International Conference on Computer Systems and Applications, 2010
This paper introduces a novel incremental approach to clustering uncertain categorical data. This... more This paper introduces a novel incremental approach to clustering uncertain categorical data. This so-called Incremental K Belief K-modes Method (IK-BKM) extends the Belief K-modes one to update the cluster partition when new information is available namely the increase of final desired clusters' number. The main objective is to update clusters' partition without complete reclustring. Our method will be illustrated by
Clustering techniques are considered as efficient tools for partitioning data sets in order to ge... more Clustering techniques are considered as efficient tools for partitioning data sets in order to get homogeneous clusters of objects. However, the reality is connected to uncertainty by nature, and these standard algorithms of clustering do not deal with this uncertainty pervaded in their parameters. In this paper we develop a clustering method in an uncertain context based on the K-modes
ABSTRACT In this paper, we investigate the problem of dynamic belief clustering. The developed ap... more ABSTRACT In this paper, we investigate the problem of dynamic belief clustering. The developed approach tackles the problem of updating the partition by decreasing the attribute set in an uncertain context. We propose a based-ranking feature selection method that allows us to preserve only the most relevant attributes. We deal with uncertainty related to attribute values, which is represented and managed through the Transferable Belief Model (TBM) concepts. The reported results showed that, in general, there is a beneficial effect of using the developed selection method to cluster dynamic feature set in comparison with the other static methods performing a complete reclustering.
ACS/IEEE International Conference on Computer Systems and Applications, 2010
This paper introduces a novel incremental approach to clustering uncertain categorical data. This... more This paper introduces a novel incremental approach to clustering uncertain categorical data. This so-called Incremental K Belief K-modes Method (IK-BKM) extends the Belief K-modes one to update the cluster partition when new information is available namely the increase of final desired clusters' number. The main objective is to update clusters' partition without complete reclustring. Our method will be illustrated by
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Papers by Sarra Hariz