An equi-biased \(k\)-prototypes algorithm for clustering mixed-type data

DOI10.1007/S12046-018-0823-0zbMath1397.62230OpenAlexW2790895082WikidataQ130125515 ScholiaQ130125515MaRDI QIDQ1789116

Hari Om, Ravi Sankar Sangam

Publication date: 10 October 2018

Published in: Sādhanā (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s12046-018-0823-0

zbMATH Keywords

data miningdata clusteringsimilarity coefficient\(k\)-prototypes

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (1)

Weighted k-prototypes clustering algorithm based on the hybrid dissimilarity coefficient

Uses Software

• UCI-ml
• SpectralCAT
• AutoClass
• COOLCAT
• ROCK

Cites Work

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• SpectralCAT: categorical spectral clustering of numerical and nominal data
• Squeezer: An efficient algorithm for clustering categorical data.
• Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number
• Incremental Clustering and Dynamic Information Retrieval
• Scalable algorithms for clustering large datasets with mixed type attributes
• Data Clustering: Theory, Algorithms, and Applications
• Data mining. Concepts and techniques
• A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters

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