[1]LIU Beibei,MA Runing,DING Jundi.Density-based statistical merging clustering algorithm[J].CAAI Transactions on Intelligent Systems,2015,10(5):712-721.[doi:10.11992/tis.201410028]
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Density-based statistical merging clustering algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 5 Page number: 712-721 Column: 学术论文—机器学习 Public date: 2015-10-25
Title:
Density-based statistical merging clustering algorithm
Author(s):
LIU Beibei1;  MA Runing1;  DING Jundi2
1. College of Science, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China;
2. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China
Keywords:
data points; density; random variable; merging; clustering algorithm; noise
CLC:
O235;TP311
DOI:
10.11992/tis.201410028
Abstract:
The ability of existing clustering algorithms to deal with noise is poor, and the speed is slow, instead this paper proposes a density-based statistical merging clustering algorithm (DSMC). The new algorithm takes each group of data points as a set of independent random variables, and gathers statistical criteria from the independent bounded difference inequality. Meanwhile, combined with the density information of the data points, the DSMC algorithm takes the descending order of the density as the merging order in the process of condensation, and thereby achieves statistical merging of different types of data points. The experimental results with both artificial datasets and real datasets show that the DSMC algorithm can not only deal with convex data set, and also has good clustering effects on nonconvex shaped, overlapped and noisy, data sets. This proves that the algorithm has good applicability and validity.
References:
[1] XU Rui, WUNSCHII D. Survey of clustering algorithms[J]. IEEE Transactions on Neural Networks, 2005, 16(3):645-678.
[2] JAIN A K, MURTY M N, FLYNN P J. Data clustering:a review[J]. Acm Computing Surveys, 1999, 31(2):264-323.
[3] MURTAGH F, CONTRERAS P. Algorithms for hierarchical clustering:an overview[J]. Wiley Interdisciplinary Reviews:Data Mining and Knowledge Discovery, 2012, 2(1):86-97.
[4] TSENG L Y, YANG S B. A genetic approach to the automatic clustering problem[J]. Pattern Recognition, 2001, 34(2):415-424.
[5] FORGY E W. Cluster analysis of multivariate data:efficiency versus interpretability of classifications[J]. Biometrics, 1965, 21:768-769.
[6] SHI J, MALIK J. Normalized cuts and image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8):888-905.
[7] BEZDEK J C, EHRLICH R, FULL W. FCM:The fuzzy c-means clustering algorithm[J]. Computers & Geosciences, 1984, 10(2-3):191-203.
[8] KRISHNAPURAM R, KELLER J M. A possibilistic approach to clustering[J]. IEEE Transactions on Fuzzy Systems, 1993, 1(2):98-110.
[9] ALPERT C J, KAHNG A B. Recent directions in netlist partitioning:a survey[J]. Integration, the VLSI Journal, 1995, 19(1):1-81.
[10] ACKERMANN M R, BL?MER J, KUNTZE D, et al. Analysis of agglomerative clustering[J]. Algorithmica, 2014, 69(1):184-215.
[11] GUHA S, RASTOGI R, SHIM K. Cure:an efficient clustering algorithm for large databases[J]. Information Systems, 2001, 26(1):35-58.
[12] ESTER M, KRIEGEL H P, SANDER J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]//Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining. Portland, USA, 1996:226-231.
[13] ZHOU Shuigeng, ZHAO Yue, GUAN Jihong, et al. A neighborhood-based clustering algorithm[M]//Advances in Knowledge Discovery and Data Mining. Berlin/Heidelberg:Springer, 2005:361-371.
[14] 马儒宁, 王秀丽, 丁军娣. 多层核心集凝聚算法[J]. 软件学报, 2013, 24(3):490-506. MA Runing, WANG Xiuli, DING Jundi. Multilevel core-sets based aggregation clustering algorithm[J]. Journal of Software, 2013, 24(3):490-506.
[15] ZHUANG Xuan, HUANG Yan, PALANIAPPAN K, et al. Gaussian mixture density modeling, decomposition, and applications[J]. IEEE Transactions on Image Processing, 1996, 5(9):1293-1302.
[16] MACLACHLAN G J, KRISHNAN T. The EM algorithm and extensions[J]. Series in Probability & Statistics, 1997, 15(1):154-156.
[17] NOCK R, NIELSEN F. Statistical region merging[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(11):1452-1458.
[18] HABIB M, MCDIARMID C, RAMIREZ-ALFONSIN J, et al. Probabilistic methods for algorithmic discrete mathematics[M]. Berlin:Springer-Verlag, 1998:1-54.
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