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Using Frequent Closed Pattern Mining to Solve a Consensus Clustering Problem

Atheer Al-Najdi 1 Nicolas Pasquier 1 Frédéric Precioso 1
1 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Projet MinD
Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : Clustering is the process of partitioning a dataset into groups based on the similarity between the instances. Many clustering algorithms were proposed, but none of them proved to provide good quality partition in all situations. Consensus clustering aims to enhance the clustering process by combining different partitions obtained from different algorithms to yield a better quality consensus solution. In this work, we propose a new consensus method that uses a pattern mining technique in order to reduce the search space from instance-based into pattern-based space. Instead of finding one solution, our method generates multiple consensus candidates based on varying the number of base clusterings considered. The different solutions are then linked and presented as a tree that gives more insight about the similarities between the instances and the different partitions in the ensemble.
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https://hal.archives-ouvertes.fr/hal-01330079
Contributor : Nicolas Pasquier <>
Submitted on : Thursday, June 9, 2016 - 9:54:12 PM
Last modification on : Tuesday, May 26, 2020 - 6:50:55 PM

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Atheer Al-Najdi, Nicolas Pasquier, Frédéric Precioso. Using Frequent Closed Pattern Mining to Solve a Consensus Clustering Problem. International Conference on Software Engineering & Knowledge Engineering, KSI Research Inc., Jul 2016, Redwood City, United States. pp.454-461, ⟨10.18293/SEKE2016-117⟩. ⟨hal-01330079⟩

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