Similarity measure design for high dimensional data

作     者：LEE Sang-hyuk YAN Sun JEONG Yoon-su SHIN Seung-soo 

作者机构：Department of Electrical and Electronic EngineeringXi'an Jiaotong-Liverpool University International Business School Suzhou Xi'an Jiaotong-Liverpool University Department of Information Communication Engineering Mokwon University Department of Information Security Tongmyong University 

出 版 物：《Journal of Central South University》 (中南大学学报（英文版）)

年 卷 期：2014年第21卷第9期

页      面：3534-3540页

核心收录：

学科分类：0810[工学-信息与通信工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0806[工学-冶金工程] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Project(RDF 11-02-03)supported by the Research Development Fund of XJTLU China 

主　　题：相似性度量 高维数据 设计 数据信息 计算结果 相似性分析 多维数据集 诈骗案 

摘      要：Information analysis of high dimensional data was carried out through similarity measure application. High dimensional data were considered as the a typical structure. Additionally, overlapped and non-overlapped data were introduced, and similarity measure analysis was also illustrated and compared with conventional similarity measure. As a result, overlapped data comparison was possible to present similarity with conventional similarity measure. Non-overlapped data similarity analysis provided the clue to solve the similarity of high dimensional data. Considering high dimensional data analysis was designed with consideration of neighborhoods information. Conservative and strict solutions were proposed. Proposed similarity measure was applied to express financial fraud among multi dimensional datasets. In illustrative example, financial fraud similarity with respect to age, gender, qualification and job was presented. And with the proposed similarity measure, high dimensional personal data were calculated to evaluate how similar to the financial fraud. Calculation results show that the actual fraud has rather high similarity measure compared to the average, from minimal 0.0609 to maximal 0.1667.