三支决策与聚类大数据分析

1、三支决策与聚类大数据分析问题求解决策过程适应不同层次问题求解的决策过程在认知和处理现实世界的问题 时, 常常采用从不同层次观察 问题的策略, 往往从极不相同 的粒度上观察和分析同一问题决策过程2聚类过程反映的就是适应不 同层次问题求解的决策过程， 即聚类过程就是在某个粒度 上决策对象元素是否属于某 个类簇的过程。聚类过程31234二支决策聚类与三支决策聚类C1 = 1, 2C2 = 3, 4Hard Clustering1234二支决策的思想二支决策聚类与三支决策聚类C1 = 1, 2, 3C2 = 3, 4C1 = 1, 2C2 = 3, 4Hard ClusteringSoft Clust

2、ering1234二支决策聚类与三支决策聚类针对那些目前知识体系下还难以决策的对象，可以在已有知识体系下给出 博弈后的一个决策结果也可以等待新信息以帮助进 一步决策三支决策的思想采用这种动态决策的思路可望为大数据处理带来新的理论模型和计算方法。61234三支决策聚类的含义决策结果是三支的：类簇的三支表示决策过程是三支的：二支决策聚类与三支决策聚类动态渐进的三支决策聚类三支决策的思想采用这种动态决策的思路可望为大数据处理带来新的理论模型和计算方法。类簇的表示Three-way decision with two setsL-RegionM-RegionR-RegionC CC, C(L(C),

3、M (C)C L(C)C L(C) M (C)A single setC1=1, 3, 4, 5, 8, 9, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 24,25,29, 30, 31, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47,48, 50, 51, 52, 53, 54, 56, 59, 60, 62C2 = 2, 6, 7, 8, 10, 14, 18, 20, 23, 26, 27, 28, 29, 31, 32, 33, 40,42, 49, 55, 57, 58, 618,2

4、0,29,31,40The dolphins social networkstLusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: The bottlenose dolphin community of doubtful sound features a large proportionof long-lasting associations. Behavioral Ecology and Sociobiology, 54(4): 396-405 (2003)9L(C1 ) = 1,

5、3, 4, 5, 7, 9, 11, 12, 13, 15, 16, 17, 19, 20, 21, 22, 24,25, 29, 30, 31, 34, 35, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 50,51, 52, 53, 54, 55, 56, 59, 60, 62,M(C1 ) = 2, 8, 40, 42,L(C2 ) = 2, 6, 10, 14, 18, 20, 23, 26, 27, 28, 32, 33, 40, 42, 49, 55,57, 58, 61,M(C2 ) = 8, 24, 29, 37.Three-way

6、representationC= (L, M)10What we can doOur work1An automatic method to determine the number of clusters usingdecision-theoretic rough set2A tree-based incremental overlapping clustering method using the three-way decision theory3Detecting and refining overlapping regions in complex networksby three-

7、way decisionsOur work1An automatic method to determine the number of clusters using decision- theoretic rough set2A tree-based incremental overlapping clustering method using the three-way decision theory3Detecting and refining overlapping regions in complex networks by three- way decisionsRepresent

8、 a community with an interval setPropose a multi-stage algorithmbased on three-way decisionsthe relationships/roles among these members (objects) in overlapping regions is differentrefining overlapping regionsdetecting different types of overlapping regionsIn working: Detecting and refining overlapp

9、ing regions incomplex networks by three-way decisions13Represent a communityPOS (C football )BND(C football )NEG(C football )C footballC, CPOS (C football ), POS (C football ) BND(C football )M-RegionL-RegionR-Region14Refining Overlapping RegionsPOB(C football ,Cbasketball )POP(C football ,Cbasketba

10、ll )fanaticthe example about football fans and basketball fans described in previous can be explained in this modelBOB(C football ,Cbasketball )amateurPOB(Cbasketball ,C football )Categorization of overlapping regionsTraditionalCategorization for overlapping regionsOverlappingMacro TypeMicro TypePOP

11、POBBOBTYPE.1ABCDTYPE.2EFTYPE.3GNonOverlappingTYPE.4HZacharys karate club network18Zacharys karate club networkResult of DOC-TWD in Zacharys karate clubOverlapping vertices between C2 and C3 in Zacharyskarate clubLancichinetti A, Fortunato S, Kertsz J. Detecting the overlapping and hierarchical commu

12、nity structure in complex networksJ. NewJournal of Physics, 2009, 11(3): 033015.Huang J, Sun H, Han J, et al. Density-based shrinkage for revealing hierarchical and overlapping community structure in networksJ.Physica A: Statistical Mechanics and its Applications, 2011, 390(11): 2160-2171.Li J, Wang

13、 X, Eustace J. Detecting overlapping communities by seed community in weighted complex networksJ. Physica A: StatisticalMechanics and its Applications, 2013, 392(23): 6125-6134.Sun P G, Gao L, Shan Han S. Identification of overlapping and non-overlapping community structure by fuzzy clustering in co

14、mplex networksJ. Information Sciences, 2011, 181(6): 1060-1071.AlgorithmOverlapping VerticesDOC-TWDPOP(C2 , C3 )9,31POB(C2 , C3 )3POB(C3 , C2 )10LFM13,9,10,14,31DenShrink210,20EM-BOAD33Sun49,10,14,20What we can doOur work1An automatic method to determine the number of clusters using decision- theore

15、tic rough set2A tree-based incremental overlapping clustering method using the three-way decision theory3Detecting and refining overlapping regions in complex networks by three-way decisionsRisk of the cluster schemen1vNumber of clustersRisk(CS)Determining the Number of Clusters Using DTRSHong Yu, Z

16、hanguo Liu, Guoyin Wang. An automatic method to determinethe number of clusters using decision-theoretic rough set.International Journal of Approximate Reasoning, 2014, 55(1): 101-115.a new clustering validity evaluation function based on the extended DTRSget the curve of the clustering qualitydeter

17、mine the number of clusters corresponding to the extremum of the curveOur work1An automatic method to determine the number of clusters using decision- theoretic rough set2A tree-based incremental overlapping clustering method using the three-way decision theory3Detecting and refining overlapping reg

18、ions in complex networks by three-way decisionsMain objective of the studyTo propose a new soft clustering algorithm using three-way decisionsTo propose strategies basedon three-way decisionsthe original data setthe incremental dataRepresentative points and construct the tree of representative point

19、s Searching and updating the treeClustering the incremental data using three-way decisionsTo propose a soft incremental clustering approachHong Yu, Cong Zhang, Guoyin Wang. A tree-based incremental overlapping clustering method using the three-way decision theory, Knowledge-Based22Systems, Vol.91, Jan. 2016ht,tpP:a/gcse.csq1up8t.9edu2.0cn3/yuhong/大数据算法在