Tree and Graph Mining

Abstract

During the past decade, we have witnessed an explosive growth in our capabilities to both generate and collect data. Various data mining techniques have been proposed and widely employed to discover valid, novel and potentially useful patterns in these data. Data mining involves the discovery of patterns, associations, changes, anomalies, and statistically significant structures and events in huge collections of data. One of the key success stories of data mining research and practice has been the development of efficient algorithms for discovering frequent itemsets – both sequential (Srikant & Agrawal, 1996) and non-sequential (Agrawal & Srikant, 1994). Generally speaking, these algorithms can extract co-occurrences of items (taking or not taking into account the ordering of items) in an efficient manner. Although the use of sets (or sequences) has effectively modeled many application domains, like market basket analysis, medical records, a lot of applications have emerged whose data models do not fit in the traditional concept of a set (or sequence), but require the deployment of richer abstractions, like graphs or trees. Such graphs or trees arise naturally in a number of different application domains including network intrusion, semantic Web, behavioral modeling, VLSI reverse engineering, link analysis and chemical compound classification. Thus, the need to extract complex tree-like or graphlike patterns in massive data collections, for instance, in bioinformatics, semistructured or Web databases, became a necessity. The class of exploratory mining tasks, which deal with discovering patterns in massive databases representing complex interactions among entities, is called Frequent Structure Mining (FSM) (Zaki, 2002). In this article we will highlight some strategic application domains where FSM can help provide significant results and subsequently we will survey the most important algorithms that have been proposed for mining graph-like and tree-like substructures in massive data collections.