Similarity measure design for high dimensional data
来源期刊：中南大学学报(英文版)2014年第9期
论文作者：LEE Sang-hyuk YAN Sun JEONG Yoon-su SHIN Seung-soo
文章页码：3534 - 3540
Key words：high dimensional data; similarity measure; difference; neighborhood information; financial fraud
Abstract: 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.
LEE Sang-hyuk1, YAN Sun2, JEONG Yoon-su3, SHIN Seung-soo4
(1. Department of Electrical and Electronic Engineering,
Xi’an Jiaotong-Liverpool University, Suzhou 215123, China;
2. International Business School Suzhou, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China;
3. Department of Information Communication Engineering, Mokwon University,
21 Mokwon-gil, Seo-gu, Daejeon, 302-318, Korea;
4. Department of Information Security, Tongmyong University, Sinseonno, Nam-gu, Busan, 608-711, Korea)
Abstract: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.
Key words:high dimensional data; similarity measure; difference; neighborhood information; financial fraud
