Cascade Evaluation of Clustering Algorithms
| Autor: | Isabelle Tellier, Fabien Torre, Olivier Bousquet, Laurent Candillier |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Computer Science::Machine Learning
Fuzzy clustering business.industry Generalization Computer science Supervised learning Correlation clustering Probabilistic logic Machine learning computer.software_genre Biclustering ComputingMethodologies_PATTERNRECOGNITION Unsupervised learning Artificial intelligence Data mining business Cluster analysis computer |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783540453758 ECML |
| DOI: | 10.1007/11871842_54 |
| Popis: | This paper is about the evaluation of the results of clustering algorithms, and the comparison of such algorithms. We propose a new method based on the enrichment of a set of independent labeled datasets by the results of clustering, and the use of a supervised method to evaluate the interest of adding such new information to the datasets. We thus adapt the cascade generalization [1] paradigm in the case where we combine an unsupervised and a supervised learner. We also consider the case where independent supervised learnings are performed on the different groups of data objects created by the clustering [2]. We then conduct experiments using different supervised algorithms to compare various clustering algorithms. And we thus show that our proposed method exhibits a coherent behavior, pointing out, for example, that the algorithms based on the use of complex probabilistic models outperform algorithms based on the use of simpler models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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