The "Best K" for entropy-based categorical data clustering

iVIBRATE: Interactive visualization-based framework for clustering large datasets

ACM Transactions on Information Systems (TOIS)

Efficiently clustering transactional data with weighted coverage density

CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management

Determining the best K for clustering transactional datasets: A coverage density-based approach

Data & Knowledge Engineering

“Best K”: critical clustering structures in categorical datasets

Knowledge and Information Systems

HE-Tree: a framework for detecting changes in clustering structure for categorical data streams

The VLDB Journal — The International Journal on Very Large Data Bases

SCALE: a scalable framework for efficiently clustering transactional data

Data Mining and Knowledge Discovery

A framework for clustering categorical time-evolving data

IEEE Transactions on Fuzzy Systems

CPCQ: Contrast pattern based clustering quality index for categorical data

Pattern Recognition

DHCC: Divisive hierarchical clustering of categorical data

Data Mining and Knowledge Discovery

Determining the number of clusters using information entropy for mixed data

Pattern Recognition