Graph-Based Data Mining

Graph-Based Data Mining

Wenyuan Li (Nanyang Technological University, Singapore), Wee-Keong Ng (Nanyang Technological University, Singapore) and Kok-Leong Ong (Deakin University, Australia)
Copyright: © 2007 |Pages: 17
DOI: 10.4018/978-1-59904-271-8.ch011
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Abstract

With the most expressive representation that is able to characterize the complex data, graph mining is an emerging and promising domain in data mining. Meanwhile, the graph has been well studied in a long history with many theoretical results from various foundational fields, such as mathematics, physics, and artificial intelligence. In this chapter, we systematically reviewed theories and techniques newly studied and proposed in these areas. Moreover, we focused on those approaches that are potentially valuable to graph-based data mining. These approaches provide the different perspectives and motivations for this new domain. To illustrate how the method from the other area contributes to graph-based data mining, we did a case study on a classic graph problem that can be widely applied in many application areas. Our results showed that the methods from foundational areas may contribute to graph-based data mining.

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