Multiple Consensuses Clustering by Iterative Merging/Splitting of Clustering Patterns
| Autor: | Frédéric Precioso, Nicolas Pasquier, Atheer Al-Najdi |
|---|---|
| Přispěvatelé: | Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Projet MinD, Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Fuzzy clustering Single-linkage clustering Correlation clustering Closed patterns [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS] 02 engineering and technology computer.software_genre Unsupervised learning Clustering [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 03 medical and health sciences CURE data clustering algorithm 020204 information systems Consensus clustering 0202 electrical engineering electronic engineering information engineering Cluster analysis Mathematics Constrained clustering Hierarchical clustering 030104 developmental biology ComputingMethodologies_PATTERNRECOGNITION Ensemble clustering Data mining computer |
| Zdroj: | Proceedings of the MLDM'2016 International Conference, Lecture Notes in Artificial Intelligence 9729 Machine Learning and Data Mining in Pattern Recognition Machine Learning and Data Mining in Pattern Recognition, Jul 2016, New York, United States. pp.790-804, ⟨10.1007/978-3-319-41920-6⟩ Machine Learning and Data Mining in Pattern Recognition ISBN: 9783319419190 MLDM |
| DOI: | 10.1007/978-3-319-41920-6⟩ |
| Popis: | International audience; The existence of many clustering algorithms with variable performance on each dataset made the clustering task difficult. Consensusclustering tries to solve this problem by combining the partitions generated by different algorithms to build a new solution that is more stable and achieves better results. In this work, we propose a new consensus method that, unlike others, give more insight on the relations between the different partitions in the clusterings ensemble, by using the frequent closed itemsets technique, usually used for association rules discovery. Instead of generating one consensus, our method generates multiple consensuses based on varying the number of base clusterings, and links these solutions in a hierarchical representation that eases the selection of the best clustering. This hierarchical view also provides an analysis tool, for example to discover strong clusters or outlier instances. |
| Databáze: | OpenAIRE |
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