Introducing the Data Mining Utility: A Web-based Application for Data Mining Experimentation on Neurological Datasets

Daniel Sanchez, A. Shirk, Danmary Sanchez, and A. Marrero (USA)


Data mining, web services, distributed computing


Data mining applications have facilitated knowledge discovery in a multitude of areas including marketing, biological and geological sciences, and image processing. Through well established mathematical algorithms related to pattern recognition, predictive modeling, and anomaly detection, warehouses of data have been explored and interpreted with the purposes of helping researchers unveil and understand complex patterns. In this paper, we present the development of a web-based Data Mining Utility, created with the ultimate goal of allowing medical researchers to detect new artifacts that have been previously obscured through conventional signal processing techniques in neurological datasets. This web tool is powered by Data to Knowledge, which was created by the Automated Learning Group of the National Center for Supercomputing Applications at the University of Illinois, by integrating a vast library of customizable data mining techniques into a unified programming environment. Using this framework, along with the D2K Web Service, this Data Mining Utility will enhance an established Multi-Site Pediatric Network for fMRI in Childhood Epilepsy, as a means for researchers to gain further insight on medical case studies.

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