An approach to graph mining using gSpan algorithm

Complex information is prevailing in every sphere of activities This call for the necessity to represent, store and manipulate complex information (e.g. detects correlations and patterns, discover explanations, construct predictive models etc.). Furthermore, being autonomously maintained, data can change in time or even change its base structure, making it difficult for representation systems to accommodate these changes. Current representation and storage systems are not very flexible in dealing with big changes and also they are not concerned with the ability of performing complex data manipulations of the sort mentioned above. On the other hand, data manipulation systems cannot easily work with structural or relational data, but just with flat data representations. We want to bridge the gap between the two, by introducing a new type of database structure, called Graph Databases (GDB), based on a natural graph representation. Our Graph Databases are able to represent as graphs any kind of information, naturally accommodate changes in data, and they also make easier for Machine Learning methods to use the stored information. Graph mining is the process of extracting sub graphs from graph database or database of graphs. The problem of discovering frequent sub graphs of graph data can be solved by constructing a candidate set of sub graphs first, and then, identifying within this candidate set those sub graphs that meet the frequent sub graph requirement.