计算机科学 ›› 2016, Vol. 43 ›› Issue (Z11): 436-442.doi: 10.11896/j.issn.1002-137X.2016.11A.098

• 信息安全 • 上一篇    下一篇

基于联系数的位置不确定性数据UCNK-Means聚类算法

王骏,黄德才   

  1. 浙江工业大学计算机科学与技术学院 杭州310023,浙江工业大学计算机科学与技术学院 杭州310023
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受水利部公益性行业科研专项(201401044)资助

UCNK-Means Clustering Method for Position Uncertain Data Based on Connection Number

WANG Jun and HUANG De-cai   

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

摘要: 摘要位置不确定性数据的聚类是一个新的不确定性数据聚类问题。其聚类方法主要包括获取对象的概率密度函数,通过积分计算对象间的期望距离来进行聚类分析和以区间数表示对象,通过区间数的系列运算来进行聚类分析这两大类。前者存在概率密度函数获取困难、计算复杂、实用性不强的缺陷;后者在区间数转化为实数过程中,忽略了区间数变化范围对聚类效果的影响,其聚类质量不佳。鉴于此,提出一种基于联系数的不确定对象聚类新算法UCNK-Means。该算法用联系数巧妙地表示不确定性对象,并专门定义了对象间的联系距离,运用联系数态势值比较联系距离大小,克服了现有算法的不足。仿真实验表明,UCNK-Means具有聚类精度高、计算复杂度低、实用性强的特点。

关键词: 不确定性数据,联系数,聚类,数据挖掘

Abstract: Clustering for position uncertain data is a new problem of uncertain data clustering.Mainly there are two solutions to this new problem.The first is clustering acquiring the probability density function or probability distribution function of uncertain object and getting the expected distance with integral operation.The second is clustering by series of operation of interval data.However,the former has the disadvantages of getting probability density function hard,complex operation and poor practicability,and the latter ignores the effect of the range of interval data to the result of clustering.Therefore,a new uncertain data clustering method UCNK-Means was put forward.This method uses connection number as the model of uncertain object and defines connection distance between two objects and uses the situationvalue to compare the connection distance,which overcome the disadvantages existed in the two solutions above.The experiment illustrates that UCNK-Means has high precision of clustering,low complexity and strong praticability.

Key words: Uncertain data,Connection number,Clustering,Data mining

[1] 周傲英,金澈清,王国仁,等.不确定性数据管理技术研究综述[J].计算机学报,2009,2(1):1-16
[2] 陆亿红,夏聪.不确定数据的最优k近邻和局部密度聚类算法[J].控制与决策,2016(3):541-546
[3] 王梁,周光焱,王黎维,等.不确定关系数据属性级溯源表示与概率计算[J].软件学报,2014,25(4):863-879
[4] Xu Lei,Hu Qing-hua,Hung E,et al.A heuristic approach to effective and efficient clustering on uncertain objects[J].Know-ledge-Based Systems,2014,66(9):112-125
[5] Wang K N,Kao Ben,Chun K C D,et al.Efficient Clustering of Uncertain Data[C]∥Proceedings of the Sixth International Conference on Data Mining(ICDM’06).IEEE,2006:436-445
[6] Hung E,Xiao Lu-rong,Hung R Y S.An Efficient Representation Model of Distance Distribution Between Uncertain Objects[C]∥Computational Intelligence,2012,8(3):373-397
[7] Xiao Lu-rong,Hom H,Hung E,et al.An Efficient Distance Calculation Method for Uncertain Objects[C]∥Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Minging.2007:10-17
[8] Yun C H,Yang J.Reducing UK-Means to K-Means[C]∥Proceedings of the 6th IEEE International Conference on Data Mi-ning(ICDM 2007).2007:483-488
[9] Gullo F,Ponti G.A Tagarelli.Clustering Uncertain Data Via K-Medoids[C]∥International Conference on Scalable Uncertainty Management.2008:229-242
[10] Chau M,Cheng R,Kao B,et al.Uncertain data mining:An example in clustering location data[C]∥The 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining.lnai,2006,3918:199-204
[11] 彭宇,罗清华,彭喜元.UIDK-means:多维不确定性测量数据聚类算法[J].仪器仪表学报,2011,32(6):1201-1207
[12] 黄德才,张丽君,赵克勤.基于a+bi型联系数的不确定网格静态调度算法[J].计算机科学,2007,34(8):126-129
[13] Su H S,Mi G S.Set pair analysis applied for identifyingpower transformer faults [C]∥IntConf on MachineLearning and Cybernetics.Dalian,2006:1708-1713
[14] Dong L,Li G G,He Z X.Pattern recognition based onall set theo-ry and SPA in complexsystem innovativecomputing[C]∥1st Int Conf.on Information and Control.Beijing,2006:204-208
[15] Huang D C,Zhao K Q.Uncertainty network planningmethodology based on the connection number a+ b i+ cj[C]∥5th World Congress on Intelligent Contrl and Automation.Hangzhou,2004:2863-2866
[16] 杨俊杰,周建中,方仍存,等.基于集对分析的不确定多属性决策方法[J].控制与决策,2008,3(12):1423-1426
[17] 刘健,刘思峰,吴顺祥.基于优势关系的多属性决策对象排序研究[J].控制与决策,2012,27(4):632-635,0
[18] 汪新凡,王坚强,杨恶恶.基于二元联系数集结算子的多准则群决策方法[J].控制与决策,2013,8(11):1630-1636
[19] 陶利民,黄德才.开放网络环境下基于多元联系数的主观信任评估与决策研究[J].小型微型计算机系统,2012,3(6):1202-1206
[20] 王文圣,金菊良,丁晶,等.水资源系统评价新方法——集对评价法[J].中国科学E辑:技术科学,2009,9(9):1529-1534
[21] Habich D,Volk P B,Diittmann R,et al.Error-a-ware density-based clustering of imprecise measurement values[C]∥Proc.of the 23nd IEEE Int’l Conf.on Data Mining(ICDM 2007).2007:417-476

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