[1]ZHOU Zhiping,WANG Jiefeng,ZHU Shuwei,et al.An improved adaptive and fast AF-DBSCAN clustering algorithm[J].CAAI Transactions on Intelligent Systems,2016,11(1):93-98.[doi:10.11992/tis.201410021]
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An improved adaptive and fast AF-DBSCAN clustering algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 1 Page number: 93-98 Column: 学术论文—机器学习 Public date: 2016-02-25
Title:
An improved adaptive and fast AF-DBSCAN clustering algorithm
Author(s):
ZHOU Zhiping;  WANG Jiefeng;  ZHU Shuwei;  SUN Ziwen
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
Keywords:
density clustering; DBSCAN; region query; global parameters; KNN distribution; mathematical statistics and analysis
CLC:
TP181
DOI:
10.11992/tis.201410021
Abstract:
The density-based DBSCAN clustering algorithm can identify clusters with arbitrary shape, however, the choice of the global parameters Eps and MinPts requires manual intervention, the process of regional query is complex and loses objects easily. Therefore, an improved density clustering algorithm with adaptive parameter for fast regional queries is proposed. Using KNN distribution and mathematical statistical analysis, the optimal global parameters Eps and MinPts are adaptively calculated, so as to avoid manual intervention and enable full automation of the clustering process. The regional query is conducted by improving the selection manner of the object, which is represented by a seed and thus avoiding manual intervention, and so the clustering efficiency is effectively increased. The experiment results looking at density clustering of four typical data sets show that the proposed method effectively improves clustering accuracy by 8.825% and reduces the average time of clustering by 0.92 s.
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