Communications of the ACM - Special issue on parallelism
C4.5: programs for machine learning
C4.5: programs for machine learning
A probabilistic framework for memory-based reasoning
Artificial Intelligence
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
COOLCAT: an entropy-based algorithm for categorical clustering
Proceedings of the eleventh international conference on Information and knowledge management
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
CLOPE: a fast and effective clustering algorithm for transactional data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
Entropy-based criterion in categorical clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
CLICKS: Mining Subspace Clusters in Categorical Data via K-Partite Maximal Cliques
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Error detection and impact-sensitive instance ranking in noisy datasets
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Towards an automatic construction of Contextual Attribute-Value Taxonomies
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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Clustering data described by categorical attributes is a challenging task in data mining applications. Unlike numerical attributes, it is difficult to define a distance between pairs of values of a categorical attribute, since the values are not ordered. In this article, we propose a framework to learn a context-based distance for categorical attributes. The key intuition of this work is that the distance between two values of a categorical attribute Ai can be determined by the way in which the values of the other attributes Aj are distributed in the dataset objects: if they are similarly distributed in the groups of objects in correspondence of the distinct values of Ai a low value of distance is obtained. We propose also a solution to the critical point of the choice of the attributes Aj. We validate our approach by embedding our distance learning framework in a hierarchical clustering algorithm. We applied it on various real world and synthetic datasets, both low and high-dimensional. Experimental results show that our method is competitive with respect to the state of the art of categorical data clustering approaches. We also show that our approach is scalable and has a low impact on the overall computational time of a clustering task.