On the Impact of Dissimilarity Measure in k-Modes Clustering Algorithm

Authors:
Michael K. Ng;Mark Junjie Li;Joshua Zhexue Huang;Zengyou He
Affiliations:
-;-;-;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2007

Citing 6
Cited 13

Algorithms for clustering data

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Pattern Recognition
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values

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A Note on K-modes Clustering

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Improving k-modes algorithm considering frequencies of attribute values in mode

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A fuzzy k-modes algorithm for clustering categorical data

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A new initialization method for categorical data clustering

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The fuzzy C-means algorithm with fuzzy P-mode prototypes for clustering objects having mixed features

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Adaptive learning of ordinal--numerical mappings through fuzzy clustering for the objects of mixed features

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Improvement of the fuzzy C-means clustering algorithm with adaptive learning of the dissimilarities among categorical feature

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G-ANMI: A mutual information based genetic clustering algorithm for categorical data

Knowledge-Based Systems
A data labeling method for clustering categorical data

Expert Systems with Applications: An International Journal
A dissimilarity measure for the k-Modes clustering algorithm

Knowledge-Based Systems
Learning from concept drifting data streams with unlabeled data

Neurocomputing
Partitive clustering (K-means family)

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Attribute value weighting in k-modes clustering

Expert Systems with Applications: An International Journal
Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number

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The k-modes type clustering plus between-cluster information for categorical data

Neurocomputing
A ranking-based algorithm for detection of outliers in categorical data

International Journal of Hybrid Intelligent Systems

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Abstract

This correspondence describes extensions to the k-modes algorithm for clustering categorical data. By modifying a simple matching dissimilarity measure for categorical objects, a heuristic approach was developed in [4], [12] which allows the use of the k-modes paradigm to obtain a cluster with strong intrasimilarity and to efficiently cluster large categorical data sets. The main aim of this paper is to rigorously derive the updating formula of the k-modes clustering algorithm with the new dissimilarity measure and the convergence of the algorithm under the optimization framework.