Clustering Mixed Data by Fast Search and Find of Density Peaks
Joint Authors
Liu, Shihua
Zhou, Bingzhong
Huang, Decai
Shen, Liangzhong
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-05
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Liu, Shihua& Zhou, Bingzhong& Huang, Decai& Shen, Liangzhong. 2017. Clustering Mixed Data by Fast Search and Find of Density Peaks. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1190617
Modern Language Association (MLA)
Liu, Shihua…[et al.]. Clustering Mixed Data by Fast Search and Find of Density Peaks. Mathematical Problems in Engineering No. 2017 (2017), pp.1-7.
https://search.emarefa.net/detail/BIM-1190617
American Medical Association (AMA)
Liu, Shihua& Zhou, Bingzhong& Huang, Decai& Shen, Liangzhong. Clustering Mixed Data by Fast Search and Find of Density Peaks. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1190617
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190617
