1. An Adaptive Method for Clustering by Fast Search-and-Find of Density Peaks
- Author
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Rashid Mehmood, Shanshan Ruan, Hussain Dawood, Jalal S. Alowibdi, and Ali Daud
- Subjects
0301 basic medicine ,Computer science ,Adaptive method ,Kernel density estimation ,02 engineering and technology ,computer.software_genre ,03 medical and health sciences ,030104 developmental biology ,Robustness (computer science) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Heat equation ,Data mining ,Cluster analysis ,computer ,Algorithm ,k-medians clustering - Abstract
Clustering by fast search and find of density peaks (DP) is a method in which density peaks are used to select the number of cluster centers. The DP has two input parameters: 1) the cutoff distance and 2) cluster centers. Also in DP, different methods are used to measure the density of underlying datasets. To overcome the limitations of DP, an Adaptive-DP method is proposed. In Adaptive-DP method, heat-diffusion is used to estimate density, cutoff distance is simplified, and novel method is used to discover exact number of cluster centers, adaptively. To validate the proposed method, we tested it on synthetic and real datasets, and comparison are done with the state of the art clustering methods. The experimental results validate the robustness and effectiveness of proposed method.
- Published
- 2017