Clustering for Probability Density Functions by New k-Medoids Method
Joint Authors
Vo-Van, T.
Nguyen-Trang, T.
Ho-Kieu, D.
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-05-09
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
This paper proposes a novel and efficient clustering algorithm for probability density functions based on k-medoids.
Further, a scheme used for selecting the powerful initial medoids is suggested, which speeds up the computational time significantly.
Also, a general proof for convergence of the proposed algorithm is presented.
The effectiveness and feasibility of the proposed algorithm are verified and compared with various existing algorithms through both artificial and real datasets in terms of adjusted Rand index, computational time, and iteration number.
The numerical results reveal an outstanding performance of the proposed algorithm as well as its potential applications in real life.
American Psychological Association (APA)
Ho-Kieu, D.& Vo-Van, T.& Nguyen-Trang, T.. 2018. Clustering for Probability Density Functions by New k-Medoids Method. Scientific Programming،Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1214663
Modern Language Association (MLA)
Ho-Kieu, D.…[et al.]. Clustering for Probability Density Functions by New k-Medoids Method. Scientific Programming No. 2018 (2018), pp.1-7.
https://search.emarefa.net/detail/BIM-1214663
American Medical Association (AMA)
Ho-Kieu, D.& Vo-Van, T.& Nguyen-Trang, T.. Clustering for Probability Density Functions by New k-Medoids Method. Scientific Programming. 2018. Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1214663
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214663