Author/Authors :
Ho-Kieu, D. Division of Computational Mathematics and Engineering - Institute for Computational Science - Ton Duc Tang University, Ho Chi Minh City, Vietnam , Vo-Van, T. Natural Science College, Can To University, Can To City, Vietnam , Nguyen-Trang, T. Division of Computational Mathematics and Engineering - Institute for Computational Science - Ton Duc Tang University, Ho Chi Minh City, Vietnam 2 Vo-Van, T. Natural Science College, Can To University, Can To City, Vietnam
Abstract :
This paper proposes a novel and efficient clustering algorithm for probability density functions based on -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.
