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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (3): 39-48    DOI: 10.11925/infotech.2096-3467.2017.0889
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A Clustering Algorithm with Adaptive Cut-off Distance and Cluster Centers
Zhen Yang(),Hongjun Wang,Yu Zhou
Electronic Engineering Institute of PLA, Hefei 230037, China
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[Objective] This paper develops a new clustering algorithm, aiming to automatically calculate the cut-off distance and select the cluster centers. [Methods] First, we proposed a new adaptive algorithm based on information entropy and the cut-off distance. Then, we extracted the cluster centers, with the help of inflection points determined by the slope trend of the weight in the sorting chart. Finally, we evaluated the performance of the ADPC algorithm to those of the DBSCAN, DPC, DGCCD, and ACP algorithms using UCI and manmade datasets. [Results] The ADPC algorithm automatically identified the cluster centers and significantly improved the precision, F-measure, normalized mutual information measurement and runtime. [Limitations] The proposed algorithm’s performance with high-dimension data as well as its efficiency to process large data sets need to be improved. [Conclusions] The proposed ADPC algorithm could effectively identify clustering centers and the cut-off distance with low-dimension or arbitrary data sets.

Key wordsClustering      Cut-off      Distance      Slope Change      ADPC     
Received: 06 September 2017      Published: 03 April 2018

Cite this article:

Zhen Yang,Hongjun Wang,Yu Zhou. A Clustering Algorithm with Adaptive Cut-off Distance and Cluster Centers. Data Analysis and Knowledge Discovery, 2018, 2(3): 39-48.

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