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Proceedings Paper

Comparing various algorithms for discovering social groups with uni-party data
Author(s): John J. Salerno; Raymond A. Cardillo; Zhongfei Mark Zhang
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Paper Abstract

The challenge of identifying important individuals and their membership as part of a group is a continuing and ever growing problem. In recent years, the data mining community has been identifying and discussing a new paradigm of data analysis using uni-party data. Within this paradigm, a methodology known as Link Discovery based on Correlation Analysis (LDCA), defines a process to compensate for the lack of relational data. CORAL, a specific implementation of LDCA, demonstrated the value of this methodology by identifying suspects involved in a Ponzi scheme with limited success. This paper introduces several new algorithms and analyzes their ability to generate a prioritized ranking of individuals involved in the Ponzi scheme based on their individual activity. To compare the accuracy of each algorithm, we present the experimental results of the algorithms, and conclude with a discussion of open issues and future activities.

Paper Details

Date Published: 28 March 2005
PDF: 12 pages
Proc. SPIE 5812, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005, (28 March 2005); doi: 10.1117/12.603680
Show Author Affiliations
John J. Salerno, Air Force Research Lab. (United States)
Raymond A. Cardillo, Air Force Research Lab. (United States)
Zhongfei Mark Zhang, SUNY/Binghamton (United States)

Published in SPIE Proceedings Vol. 5812:
Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005
Belur V. Dasarathy, Editor(s)

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