Extraction of Frequent Subgraph from Graph Database
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2014, Vol 3, Issue 3
Abstract
Graphs are promising abstraction of complex structured and semi-structured data. Graph mining techniques extract, analyze and summarize significant and useful information from the graph databases. Finding frequent subgraph from graph database is an essence of graph mining. Sometimes the mined subgraphs are large in numbers, posing difficulty in selecting significant subgraph. Every frequent subgraph is not always significant from the application perspective. This paper proposes an innovative concept to extract significant subgraphs. Our method does this in two stages. In the first stage, frequent subgraphs are identified using frequency threshold (ϴ), which is an input parameter. In the second stage, feature vectors of subgraphs are generated to calculate its statistical significance. P-value is measure of statistical significance.
Authors and Affiliations
Sakshi S. Mandke, Sheetal S. Sonawane
Keywords
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