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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
Yang Zhen(), Wang Hongjun, Zhou Yu
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
ZTFLH:  TP391  

Cite this article:

Yang Zhen,Wang Hongjun,Zhou Yu. 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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数据集 样本数 维数 类别数
L3 312 2 3
R15 600 2 15
数据集 样本数 维数 类别数
Iris 150 4 3
Aggregation 788 2 7
Waveform 5 000 21 3
Wine 178 13 3
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