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Clustering Categorical Data Using Community Detection Techniques
Author
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-21
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
With the advent of the k-modes algorithm, the toolbox for clustering categorical data has an efficient tool that scales linearly in the number of data items.
However, random initialization of cluster centers in k-modes makes it hard to reach a good clustering without resorting to many trials.
Recently proposed methods for better initialization are deterministic and reduce the clustering cost considerably.
A variety of initialization methods differ in how the heuristics chooses the set of initial centers.
In this paper, we address the clustering problem for categorical data from the perspective of community detection.
Instead of initializing k modes and running several iterations, our scheme, CD-Clustering, builds an unweighted graph and detects highly cohesive groups of nodes using a fast community detection technique.
The top-k detected communities by size will define the k modes.
Evaluation on ten real categorical datasets shows that our method outperforms the existing initialization methods for k-modes in terms of accuracy, precision, and recall in most of the cases.
American Psychological Association (APA)
Nguyen, Huu Hiep. 2017. Clustering Categorical Data Using Community Detection Techniques. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1141202
Modern Language Association (MLA)
Nguyen, Huu Hiep. Clustering Categorical Data Using Community Detection Techniques. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1141202
American Medical Association (AMA)
Nguyen, Huu Hiep. Clustering Categorical Data Using Community Detection Techniques. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1141202
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141202