Rough cluster algorithm based on kernel function
作者： Tao Zhou , Yanning Zhang , Huiling Lu , Fang’an Deng , Fengxiao Wang
DOI: 10.1007/978-3-540-79721-0_27
关键词: Canopy clustering algorithm 、 k-medians clustering 、 Correlation clustering 、 Mathematics 、 Pattern recognition 、 Single-linkage clustering 、 Data stream clustering 、 Cluster analysis 、 Fuzzy clustering 、 CURE data clustering algorithm 、 Artificial intelligence
摘要: By means of analyzing kernel clustering algorithm and rough set theory, a novel algorithm, k-means was proposed for analysis. Through using Mercer functions, samples in the original space were mapped into highdimensional feature space, which difference among these sample strengthened through mapping, combining with to cluster space. These assigned up-approximation or low-approximation corresponding centers, then data that combined update center. this method, precision improved, convergence speed fast compared classical algorithms The results simulation experiments show feasibility effectiveness algorithm.
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sci-hub.st 参考文章(18)
Z W Da, J L Cheng, Z Li, KERNEL CLUSTERING ALGORITHM Chinese Journal of Computers. ,vol. 25, pp. 587- 590 ,(2002)
Zdzisław Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data ,(1991)
J. A. Hartigan, M. A. Wong, A K-Means Clustering Algorithm Journal of The Royal Statistical Society Series C-applied Statistics. ,vol. 28, pp. 100- 108 ,(1979) , 10.2307/2346830
Pawan Lingras, Chad West, None, Interval Set Clustering of Web Users with Rough K -Means intelligent information systems. ,vol. 23, pp. 5- 16 ,(2004) , 10.1023/B:JIIS.0000029668.88665.1A
O. El Badawy, M.S. Kamel, Hierarchical representation of 2-D shapes using convex polygons: a contour-based approach Pattern Recognition Letters. ,vol. 26, pp. 865- 877 ,(2005) , 10.1016/J.PATREC.2004.09.031
Pierre Hansen, Brigitte Jaumard, Cluster analysis and mathematical programming Mathematical Programming. ,vol. 79, pp. 191- 215 ,(1997) , 10.1007/BF02614317
Rajen B. Bhatt, M. Gopal, On fuzzy-rough sets approach to feature selection Pattern Recognition Letters. ,vol. 26, pp. 965- 975 ,(2005) , 10.1016/J.PATREC.2004.09.044
Georg Peters, Some refinements of rough k-means clustering Pattern Recognition. ,vol. 39, pp. 1481- 1491 ,(2006) , 10.1016/J.PATCOG.2006.02.002
David L. Davies, Donald W. Bouldin, A Cluster Separation Measure IEEE Transactions on Pattern Analysis and Machine Intelligence. ,vol. 1, pp. 224- 227 ,(1979) , 10.1109/TPAMI.1979.4766909
Stéphane Canu, Alex Smola, Kernel methods and the exponential family Neurocomputing. ,vol. 69, pp. 714- 720 ,(2006) , 10.1016/J.NEUCOM.2005.12.009