An Improved CFSFDP Algorithm with Cluster Center Automatically Selected Based on Weighted Average Method

Motived by that clustering by fast search and find of density peaks algorithm(CFSFDP) could not select the cluster center automatically, an improved CFSFDP algorithm was presented. The improved method uses the weighted average method and calculates the weighted averages of center weight for each point as the screening indices of cluster center. The data point will be selected as cluster center if its center weight is greater than the calculated weighted average. With aforesaid method, the improved algorithm could select the cluster center automatically, and effectively avoid the err by human made according to the decision graph. Simulation experiments comparing with K-means and CFSFDP on 4 kinds of UCI datasets are shown that the improved algorithm could retain the accurate and fast characteristics of CFSFDP and select the cluster center automatically.

[1]  Bo Wu,et al.  An efficient grid-based clustering method by finding density peaks , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[2]  Li Dazi.  Modified Global K'-means Algorithm and Its Application to Data Clustering , 2011 .

[3]  Ma Tao,et al.  Improved Density Peaks Based Clustering Algorithm with Strategy Choosing Cluster Center Automatically , 2016 .

[4]  Hongjie Jia,et al.  Research on data stream clustering algorithms , 2013, Artificial Intelligence Review.

[5]  Yi Wang,et al.  Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications , 2016, IEEE Transactions on Smart Grid.

[6]  Yingjie Tian,et al.  A Comprehensive Survey of Clustering Algorithms , 2015, Annals of Data Science.

[7]  Shuliang Wang,et al.  Clustering by Fast Search and Find of Density Peaks with Data Field , 2016 .

[8]  Shuliang Wang,et al.  Comment on "Clustering by fast search and find of density peaks" , 2015, ArXiv.

[9]  Jing Gao,et al.  ICFS: An Improved Fast Search and Find of Density Peaks Clustering Algorithm , 2016, 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech).

[10]  Tu Linli,et al.  A K-Means Clustering Algorithm Based on Double Attributes of Objects , 2015, 2015 Seventh International Conference on Measuring Technology and Mechatronics Automation.

[11]  Tao Ma,et al.  A new important-place identification method , 2015, 2015 IEEE International Conference on Computer and Communications (ICCC).

[12]  Rongfang Bie,et al.  Fuzzy Clustering by Fast Search and Find of Density Peaks , 2015, IIKI.

[13]  Hao Wu,et al.  Clustering Assisted Fundamental Matrix Estimation , 2015, ArXiv.

[14]  Rongfang Bie,et al.  Clustering by fast search and find of density peaks via heat diffusion , 2016, Neurocomputing.