Modeling Relational Data as Graphs for Mining

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Modeling Relational Data as Graphs for Mining
The focus of this paper is to develop algorithms and a framework for modeling transactional data stored in relational database into graphs for mining. Most of the real-world transactions (e.g., money withdrawal, travel, phone calls) are recorded as individual transactions which needs to be transformed into a graph based on structural relationships embedded in them. We present a graph representation that not only preserves all information embedded in a database, but also removes ambiguity and information redundancy. We present a suite of space- and time-efficient algorithms for modeling graphs from relational data. Extensive experimental analysis shows the scalability of our approaches. From a pragmatic viewpoint, our framework separates database-specific aspects from modeling aspects to make it applicable for all database systems. Real-world data has been used for generating graphs and mining them for various patterns.
Subhesh Pradhan, Sharma Chakravarthy, Aditya Telan
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2009
Authors Subhesh Pradhan, Sharma Chakravarthy, Aditya Telang
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