Belief Hierarchical Clustering

TL;DR

This paper introduces a belief function-based hierarchical clustering method for uncertain data, enabling objects to belong to multiple clusters with associated belief degrees, demonstrated as effective through real data experiments.

Contribution

It presents a novel belief hierarchical clustering approach that handles uncertainty and allows multi-cluster memberships, filling a gap in standard clustering methods.

Findings

Effective clustering of uncertain data demonstrated

Objects can belong to multiple clusters with belief degrees

Method shows promising results on real datasets

Abstract

In 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.