A COMPARATIVE STUDY OF FREQUENT SUBGRAPH MINING ALGORITHMS

Data mining algorithms are facing the challenge to deal with an increasing number of complex objects. Graph is a natural data structure used for modeling complex objects. Frequent subgraph mining is another active research topic in data mining . A graph is a general model to represent data and has been used in many domains like cheminformatics and bioinformatics. Mining patterns from graph databases is challenging since graph related operations, such as subgraph testing, generally have higher time complexity than the corresponding operations on itemsets, sequences, and trees. Many frequent subgraph Mining algorithms have been proposed. SPIN, SUBDUE, g_Span, FFSM, GREW are a few to mention. In this paper we present a detailed survey on frequent subgraph mining algorithms, which are used for knowledge discovery in complex objects and also propose a frame work for classification of these algorithms. The purpose is to help user to apply the techniques in a task specific manner in various application domains and to pave wave for further research.

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