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One of the main techniques used in data mining is data clustering, which has many applications in computer science, biology, and social sciences. Constrained clustering is a type of clustering in which side information provided by the user is incorporated into current clustering algorithms. One of the well researched constrained clustering algorithms is called microaggregation. In a microaggreg...

This paper presents an algorithm and a software application for matching complex scenes in consumer image collections. By applying appropriate constraints to detected keypoint matches between a target and reference image, complex scenes from users’ collections can be matched with very good precision. Experimental results confirm the effectiveness of this approach. The method can be further enha...

Résumé. Dans de nombreuses applications, une co-classification est plus facile à interpréter qu’une classification mono-dimensionnelle. Il s’agit de calculer une bi-partition ou collection de co-clusters : chaque co-cluster est un groupe d’objets associé à un groupe d’attributs et les interprétations peuvent s’appuyer naturellement sur ces associations. Pour exploiter la connaissance du domaine...

Clustering algorithms conduct a search through the space of possible organizations of a data set. In this paper, we propose two types of instance-level clustering constraints – must-link and cannot-link constraints – and show how they can be incorporated into a clustering algorithm to aid that search. For three of the four data sets tested, our results indicate that the incorporation of surpris...