CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering Categorical Data: An Approach Based on Dynamical Systems
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
CLUC: a natural clustering algorithm for categorical datasets based on cohesion
Proceedings of the 2006 ACM symposium on Applied computing
Top-Down Parameter-Free Clustering of High-Dimensional Categorical Data
IEEE Transactions on Knowledge and Data Engineering
Discovering frequent itemsets by support approximation and itemset clustering
Data & Knowledge Engineering
Efficient layered density-based clustering of categorical data
Journal of Biomedical Informatics
Context-Based Distance Learning for Categorical Data Clustering
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Discovering pattern-based subspace clusters by pattern tree
Knowledge-Based Systems
Subspace clustering of microarray data based on domain transformation
VDMB'06 Proceedings of the First international conference on Data Mining and Bioinformatics
From Context to Distance: Learning Dissimilarity for Categorical Data Clustering
ACM Transactions on Knowledge Discovery from Data (TKDD)
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We present a novel algorithm called CLICKS, that finds clusters in categorical datasets based on a search for k-partite maximal cliques. Unlike previous methods, CLICKS mines subspace clusters. It uses a selective vertical method to guarantee complete search. CLICKS outperforms previous approaches by over an order of magnitude and scales better than any of the existing method for high-dimensional datasets. We demonstrate this improvement in an excerpt from our comprehensive performance studies.