Classification and Analysis of Frequent Subgraphs Mining Algorithms

Abstract

In recent years, data mining in graphs or graph mining have attracted much attention due to explosive growth in generating graph databases. The graph database is one type of database that consists of either a single large graph or a number of relatively small graphs. Some applications that produce graph database are as follows: Biological networks, semantic web and behavioral modeling. Among all patterns occurring in graph database, mining frequent subgraphs is of great importance. The frequent subgraph is the one that occurs frequently in the graph database. Frequent subgraphs not only are important themselves but also are applicable in other aspects of data analysis and data mining tasks, such as similarity search in graph database, graph clustering, classification, indexing, etc. So far, numerous algorithms have been proposed for mining frequent subgraphs. This study aims to create overall view of the algorithms through the analysis and comparison of their characterizations. To achieve the aim, the existing algorithms are classified based on their graph database and their subgraph generation way. The proposed classification can be effective in choosing applications appropriate algorithms and determination of graph mining new methods in this regard.