Clustering for Probability Density Functions by New k-Medoids Method
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 prop...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2018-01-01
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| Series: | Scientific Programming |
| Online Access: | http://dx.doi.org/10.1155/2018/2764016 |