Effective Frequent Subgraph Extraction applying the Weighted Graph in Big Data

Mining big data has become an important problem in the graph pattern mining research area. Inorder to select useful features, recent graph mining techniques applies repeated mining of frequent subgraphs either by varying minimum supports or by dividing a graph database recursively. Frequent subgraph mining is an import task for exploratory data analysis on graph database. Frequent subgraph mining entails two significant overheads. It is concerted with candidate set generation and isomorphism checking. Finding subgraph isomorphism is an important problem in many applications which deal with data modeled asgraphs. In this work, we propose to reduced the search space and address isomorphism overheads, a weighted approach to subgraph mining. The objective of this work is to investigate the benefits that the concept of weighted frequent subgraph mining can offer in the context of the graph model based classification. Weighted subgraphs are graps where some of the vertexes or edges are considered to be more significant than others. Received (January 12, 2016), Review Result (January 26, 2016) Accepted (February 2, 2016), Published (March 31, 2016) 430-742 Division of Computer Engineering, SungKyul Univ., Gyeonggi-do, Anyong-si, Korea email: jkcho@sungkyul.ac.kr Effective Frequent Subgraph Extraction applying the Weighted Graph in Big Data Copyright c 2016 HSST 62