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What is Clustering Algorithms for Categorical Data about?

This document summarizes research on clustering algorithms and similarity measures for categorical data. It describes 10 clustering algorithms, including K-modes, fuzzy K-modes, and ROCK. It evaluates the time complexity of these algorithms and discusses various similarity measures for categorical data. The performance of algorithms and measures is evaluated on real-world datasets like mushroom, zoo, soybean, cancer, vote, car and iris by measuring cluster accuracy and error rate.

Author

Neti Suherawati

Language

EN