Algorithms for clustering data
Algorithms for clustering data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generality-Based Conceptual Clustering with Probabilistic Concepts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
Efficient Local Search in Conceptual Clustering
DS '01 Proceedings of the 4th International Conference on Discovery Science
Parameter-Free Hierarchical Co-clustering by n-Ary Splits
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Parameter-less co-clustering for star-structured heterogeneous data
Data Mining and Knowledge Discovery
Hierarchical co-clustering: off-line and incremental approaches
Data Mining and Knowledge Discovery
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Unsupervised clustering algorithms aims to synthesize a dataset such that similar objects are grouped together whereas dissimilar ones are separated. In the context of data analysis, it is often interesting to have tools for interpreting the result. There are some criteria for symbolic attributes which are based on the frequency estimation of the attribute-value pairs. Our point of view is to integrate the construction of the interpretation inside the clustering process. To do this, we propose an algorithm which provides two partitions, one on the set of objects and the second on the set of attribute-value pairs such that those two partitions are the most associated ones. In this article, we present a study of several functions for evaluating the intensity of this association.