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Fast Density Clustering Algorithm for Numerical Data and Categorical Data
Joint Authors
Jinyin, Chen
Huihao, He
Jungan, Chen
Shanqing, Yu
Zhaoxia, Shi
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-03-26
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Data objects with mixed numerical and categorical attributes are often dealt with in the real world.
Most existing algorithms have limitations such as low clustering quality, cluster center determination difficulty, and initial parameter sensibility.
A fast density clustering algorithm (FDCA) is put forward based on one-time scan with cluster centers automatically determined by center set algorithm (CSA).
A novel data similarity metric is designed for clustering data including numerical attributes and categorical attributes.
CSA is designed to choose cluster centers from data object automatically which overcome the cluster centers setting difficulty in most clustering algorithms.
The performance of the proposed method is verified through a series of experiments on ten mixed data sets in comparison with several other clustering algorithms in terms of the clustering purity, the efficiency, and the time complexity.
American Psychological Association (APA)
Jinyin, Chen& Huihao, He& Jungan, Chen& Shanqing, Yu& Zhaoxia, Shi. 2017. Fast Density Clustering Algorithm for Numerical Data and Categorical Data. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1191382
Modern Language Association (MLA)
Jinyin, Chen…[et al.]. Fast Density Clustering Algorithm for Numerical Data and Categorical Data. Mathematical Problems in Engineering No. 2017 (2017), pp.1-15.
https://search.emarefa.net/detail/BIM-1191382
American Medical Association (AMA)
Jinyin, Chen& Huihao, He& Jungan, Chen& Shanqing, Yu& Zhaoxia, Shi. Fast Density Clustering Algorithm for Numerical Data and Categorical Data. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1191382
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1191382