Frequent subgraph mining algorithms: Framework, classification, analysis, comparisons

Abstract

Graphs and Trees are non-linear data structures used to organise, model and solve many real world problems and becoming more popular both in scientific as well as commercial domains. They have wide number of applications ranging from Telephone networks, Internet, Social Networks, Program flow, Chemical Compounds, BioInformatics, XML data, Terrorist networks etc. Graph Mining is used for finding useful and significant patterns. Frequent subgraph Mining mines for frequent patterns and subgraphs and they form the basis for Graph clustering, Graph classification, Graph Based Anomaly Detection. In this paper, classification of FSM algorithms is done and popular frequent subgraph mining algorithms are discussed. Comparative study of algorithms is done by taking chemical compounds dataset. Further, this paper provides a framework which acts as strong foundation in understanding any frequent subgraph mining algorithm.