Symbolic clustering using a new dissimilarity measure
Pattern Recognition
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
COOLCAT: an entropy-based algorithm for categorical clustering
Proceedings of the eleventh international conference on Information and knowledge management
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Data Reduction Method for Categorical Data Clustering
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Aggregate distance based clustering using fibonacci series-FIBCLUS
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
Integrative parameter-free clustering of data with mixed type attributes
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Clustering in data mining is a discovery process that groups a set of data so as to maximize the intra-cluster similarity and to minimize the inter-cluster similarity. Clustering becomes more challenging when data are categorical and the amount of available memory is less than the size of the data set. In this paper, we introduce CBC (Clustering Based on Compressed Data), an extension of the Birch algorithm whose main characteristics refer to the fact that it can be especially suitable for very large databases and it can work both with categorical attributes and mixed features. Effectiveness and performance of the CBC procedure were compared with those of the well-known K-modes clustering algorithm, demonstrating that the CBC summary process does not affect the final clustering, while execution times can be drastically lessened.