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Mathematical Problems in Engineering
Volume 2017, Article ID 6393652, 15 pages
Research Article

Fast Density Clustering Algorithm for Numerical Data and Categorical Data

1Zhejiang University of Technology, Zhejiang 310023, China
2Electrical Engineering Department, Ningbo Wanli University, Ningbo 310023, China

Correspondence should be addressed to Chen Jinyin; nc.ude.tujz@niynijnehc

Received 20 August 2016; Revised 2 January 2017; Accepted 15 January 2017; Published 26 March 2017

Academic Editor: Erik Cuevas

Copyright © 2017 Chen Jinyin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.