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A bi-clustering framework for categorical data

Céline Robardet 1 Ruggero G Pensa 1 Jean-François Boulicaut 1
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Bi-clustering is a promising conceptual clustering approach. Within categorical data, it provides a collection of (possibly overlapping) bi-clusters, i.e., linked clusters for both objects and attribute-value pairs. We propose a generic framework for bi-clustering which enables to compute a bi-partition from collections of local patterns which capture locally strong associations between objects and properties. To validate this framework, we have studied in details the instance CDK-Means. It is a K-Means-like clustering on collections of formal concepts, i.e., connected closed sets on both dimensions. It enables to build bi-partitions with a user control on overlapping between bi-clusters. We provide an experimental validation on many benchmark datasets and discuss the interestingness of the computed bi-partitions.
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Contributor : Céline Robardet <>
Submitted on : Friday, June 9, 2017 - 10:29:21 AM
Last modification on : Wednesday, July 8, 2020 - 12:43:50 PM

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Céline Robardet, Ruggero G Pensa, Jean-François Boulicaut. A bi-clustering framework for categorical data. 9th European Conf. on Principles and Practice of Knowledge Discovery in Databases, PKDD'05, Sep 2005, Porto, Portugal. pp.643-650, ⟨10.1007/11564126_68⟩. ⟨hal-01535565⟩



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