Discovering Structural Patterns in Telecommunications Data

Andi Baritchi, Diane J. Cook, and Lawrence B. Holder, University of Texas at Arlington, USA

With the increasing amount and complexity of data being collected, there is an urgent need to create automated techniques for mining the data. In particular, data being generated and stored by telecom companies overwhelms scientists’ ability to manually discover patterns in the data. Because much of this data is structural in nature, or composed of parts and relations between the parts, linear attribute-value based algorithms will not capture all of the intricacies of the data. Hence, there exists a need to develop scalable tools to analyze and discover concepts in structural databases.


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