A k-Means-Like Algorithm for Clustering Categorical Data Using an Information Theoretic-Based Dissimilarity Measure

DOI10.1007/978-3-319-30024-5_7zbMATH Open1475.68288OpenAlexW2418423341MaRDI QIDQ2807075

Van-Nam Huynh, Thu Hien Nguyen

Publication date: 19 May 2016

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-30024-5_7

zbMATH Keywords

cluster analysis\(K\)-meansdissimilarity measurescategorical data clustering

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Title not available (Why is that?)

Uses Software

• UCI-ml

Recommendations

• Title not available (Why is that?) 👍 👎
• Title not available (Why is that?) 👍 👎
• A \(k\)-populations algorithm for clustering categorical data 👍 👎
• Clustering with respect to the information distance 👍 👎
• Comparing clusterings -- an information based distance 👍 👎
• Clustering Categorical Data via Ensembling Dissimilarity Matrices 👍 👎
• The Clustering of Categorical Data: A Comparison of a Model-based and a Distance-based Approach 👍 👎

This page was built for publication: A k-Means-Like Algorithm for Clustering Categorical Data Using an Information Theoretic-Based Dissimilarity Measure

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q2807075)