多尺度聚类挖掘算法  

Multi-scale Clustering Mining Algorithm

作　　者：韩玉辉[1,2,3] 赵书良[1,2,3] 柳萌萌[1,2,3] 罗燕[1,2,3] 丁亚飞[1,2,3] HAN Yu-hui ZHAO Shu-liang LIU Meng-meng LUO Yan DING Ya-fei(College of Mathematics ＆ Information Science, Hebei Normal University, Shijiazhuang 050024, China Hebei Key Laboratory of Computational Mathematics ＆ Applications, Hebei Normal University, Shijiazhuang 050024, China Institute of Mobile Internet of Things, Hebei Normal University,Shijiazhuang 050024,China)

机构地区：[1]河北师范大学数学与信息科学学院,石家庄050024 [2]河北师范大学河北省计算数学与应用重点实验室,石家庄050024 [3]河北师范大学移动物联网研究院,石家庄050024

出　　处：《计算机科学》2016年第8期244-248,共5页Computer Science

基　　金：国家自然科学基金项目(71271067);国家社会科学基金项目(13BTY011);国家社科基金重大项目(13&ZD091);河北省高等学校科学技术研究项目(QN2014196);河北师范大学硕士基金(201402002)资助

摘　　要：数据挖掘领域在多尺度研究上已取得了一些进展。然而,当前研究主要集中于空间、图像数据的多尺度挖掘,并且传统的聚类挖掘并未对数据集的多尺度特性进行单独的研究。针对存在的问题,进行了普适性的多尺度聚类挖掘理论和方法的研究。首先,根据概念分层理论扩展尺度定义并构建多尺度数据集;其次,阐述尺度转换原因、分类,归纳多尺度聚类的定义;然后,以克里格法为理论基础,给出多尺度聚类尺度上推算法MSCSUA和多尺度聚类尺度下推算法MSCSDA;最后,利用公用UCI聚类数据集和H省全员人口真实数据集对算法进行实验验证,结果表明MSCSUA和MSCSDA是有效、可行的。Data mining field has made some progress on the multi-scale research. However, the current research mostly focuses on the multi-scale mining of the space or image data. And traditional clustering mining has not separately stu- died the multi-scale characteristic of datasets. According to existing problems, this paper carried on the general study of multi-scale clustering mining theories and methods. Firstly, we extended scale definition on the basis of the concept hierar- chy and built multi-scale datasets. Secondly, we expounded the reasons and classification of scale conversion, meanwhile concluded the definition of the multi-scale clustering. Then, we introduced multi-scale clustering scaling up algorithm and multi-scale clustering scaling down algorithm based on the kriging theories. Finally, simulation experiments tested MSCSUA and MSCSDA with the help of public UCI clustering datasets and demographic dataset from H province. And the experimental results show that MSCSUA and MSCSDA are effective and feasible.

关 键 词：多尺度 聚类 尺度转换 多尺度聚类挖掘 克里格法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]