Computer Science ›› 2016, Vol. 43 ›› Issue (7): 255-258.doi: 10.11896/j.issn.1002-137X.2016.07.046

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Improved Density Peaks Based Clustering Algorithm with Strategy Choosing Cluster Center Automatically

MA Chun-lai, SHAN Hong and MA Tao   

  • Online:2018-12-01 Published:2018-12-01

Abstract: A new density peaks based clustering method (CFSFDP) was introduced in the paper.For the problem that it is difficult to decide the cluster number with CFSFDP,an improved algorithm was presented.With a cluster center automatic choosing strategy,the algorithm search for the “turning points” with the trends of cluster center weight’s changing.Then we could regard a set of points whose weight is bigger than “turning points” as the cluster center.The error brought by ruling in the decision graph could be avoided with the strategy.Experiment was done to compare to DBSCAN and CFSFDP with 5 kinds of datasets.The results show that the improved algorithm has better performance in accuracy and robustness,and can be applied in clustering analysis for low dimension data.

Key words: Clustering,DBSCAN,Density peak,Cluster center

[1] Jain A K.Data clustering:50 years beyond K-means[J].Pattern Recognition Letters,2010,31(8):651-666
[2] Kriegel H P,Krger P,Sander J,et al.Density-based clustering[J].Wiley Interdisciplinary Reviews:Data Mining and Know-ledge Discovery,2011,1(3):231-240
[3] Achtert E,Goldhofer S,Kriegel H P,et al.Evaluation of clusterings--metrics and visual support[C]∥2012 IEEE 28th International Conference on Data Engineering (ICDE).IEEE,2012:1285-1288
[4] Kaufman L,Rousseeuw P J.Finding groups in data:an introduction to cluster analysis(344 ed)[M].John Wiley & Sons,2009
[5] Li Cui-xia,Shi Wei-hang,Li Zhan-bo.Density Based WeightedFuzzy Clustering Algorithm[J].Computer Science.2012,39(5):180-182(in Chinese) 李翠霞,史苇杭,李占波.一种基于密度的加权模糊均值聚类算法[J].计算机科学,2012,39(5):180-182
[6] Parimala M,Lopez D,Senthilkumar N C.A survey on density based clustering algorithms for mining large spatial databases[J].International Journal of Advanced Science and Technology,2011,31(1):59-66
[7] Braune C,Besecke S,Kruse R.Density Based Clustering:Alternatives to DBSCAN[M].Springer International Publishing,2015
[8] Kisilevich S,Mansmann F,Keim D.P-DBSCAN:a density based clustering algorithm for exploration and analysis of attractive areasusing collections of geo-tagged photos[C]∥Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application.ACM,2010:591-598
[9] Kumar N,Sivasathya S.Density-Based Spatial Clustering with Noise-A Survey[J].International Journal of Computer Science and Mobile Computing,2014,3(3):1004-1011
[10] Zhou H,Wang P,Li H.Research on Adaptive Parameters Determination in DBSCAN Algorithm[J].Journal of Information &Computational Science,2012,9(7):1967-1973
[11] Smiti A,Elouedi Z.DBSCAN-GM:An improved clustering me-thod based on Gaussian Means and DBSCAN techniques[C]∥2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES).IEEE,2012:573-578
[12] Zhang Li-jie.Stable saturation clensity of DBSCAN algorithm[M].Application Research of Computers,2014(7):1972-1975(in Chinese) 张丽杰.具有稳定饱和度的DBSCAN算法[J].计算机应用研究,2014(7):1972-1975
[13] Tran T N,Drab K,Daszykowski M.Revised DBSCAN algorithm to cluster data with dense adjacent clusters[J].Chemometrics and Intelligent Laboratory Systems,2013,120:92-96
[14] Rodriguez A,Laio A.Clustering by fast search and find of densitypeaks[J].Science,2014,344(6191):1492-1496
[15] Gionis A,Mannila H,Tsaparas P.Clustering aggregation[J].ACM Transactions on Knowledge Discovery from Data (TKDD),2007,1(1):341-352
[16] Powers D M.Evaluation:from precision,recall and F-measure to ROC,informedness,markedness and correlation[J].Journal of Macline Learning Technologies,2008,2:2229-3981

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