Open AccessEnhancing Sub Graph Matching With Set Correlation Technique in Large Graph Database
Author(s) -
Bharti Nana Durgade,
Krishna Kadam
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.5.20000
Subject(s) - computer science , correlation , graph , matching (statistics) , similarity (geometry) , data mining , pruning , information retrieval , theoretical computer science , artificial intelligence , mathematics , statistics , geometry , agronomy , image (mathematics) , biology
As the increase of users in social networking sites gives rise to complex relation between the users, which often leads to understand the group of users with similar taste. Now days, this is a one of the growing research area to find the similar users in the relational graph database. Many systems are been introduced to identify the matching sub graphs using similarity between the users. This often yields not much appropriate results due to strict similarity measures. So proposed system uses a technique of identifying correlation between the users for the fired query using pattern identification by incorporating frequent pattern analysis and Pearson correlation which is catalyzed by strong pruning techniques.