We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Clustering for Probability Density Functions by New k-Medoids Method.
- Authors
Ho-Kieu, D.; Vo-Van, T.; Nguyen-Trang, T.
- Abstract
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.
- Subjects
PROBABILITY density function; CLUSTER analysis (Statistics); ALGORITHMS; RAND index; PROBABILITY in quantum mechanics; MATHEMATICAL statistics
- Publication
Scientific Programming, 2018, p1
- ISSN
1058-9244
- Publication type
Article
- DOI
10.1155/2018/2764016