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Conference Papers Year : 2020

Overlapping Hierarchical Clustering (OHC)

Abstract

Agglomerative clustering methods have been widely used by many research communities to cluster their data into hierarchical structures. These structures ease data exploration and are understandable even for non-specialists. But these methods necessarily result in a tree, since, at each agglomeration step, two clusters have to be merged. This may bias the data analysis process if, for example, a cluster is almost equally attracted by two others. In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and combines the advantages of hierarchies with the precision of a less arbitrary clustering. We validate our work with extensive experiments on real data sets and compare it with existing tree-based methods, using a new measure of similarity between heterogeneous hierarchical structures.
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Dates and versions

hal-02452729 , version 1 (29-05-2020)

Identifiers

  • HAL Id : hal-02452729 , version 1

Cite

Ian Jeantet, Zoltan Miklos, David Gross-Amblard. Overlapping Hierarchical Clustering (OHC). Inteligent Data Analysis (IDA 2020), Apr 2020, Konstanz, Germany. ⟨hal-02452729⟩
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