Clustering Mixed Data by Fast Search and Find of Density Peaks-Reference-Cited by-同舟云学术

Clustering Mixed Data by Fast Search and Find of Density Peaks

Published:2017 Issue: Volume:2017 Page:1-7
ISSN:1024-123X
Container-title:Mathematical Problems in Engineering
language:en
Short-container-title:Mathematical Problems in Engineering

Author:

Liu Shihua¹²^ORCID,Zhou Bingzhong²,Huang Decai¹^ORCID,Shen Liangzhong³^ORCID

Affiliation:

1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

2. Department of Information Technology, Wenzhou Vocational & Technical College, Wenzhou, China

3. School Administration Offices, Wenzhou Business College, Wenzhou, China

Abstract

Aiming at the mixed data composed of numerical and categorical attributes, a new unified dissimilarity metric is proposed, and based on that a new clustering algorithm is also proposed. The experiment result shows that this new method of clustering mixed data by fast search and find of density peaks is feasible and effective on the UCI datasets.

Funder

Ministry of Water Resources

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

Link

http://downloads.hindawi.com/journals/mpe/2017/5060842.pdf

Reference11 articles.

1. SpectralCAT: Categorical spectral clustering of numerical and nominal data

2. Squeezer: An efficient algorithm for clustering categorical data

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