ADaM: a data mining toolkit for scientists and engineers

Authors:
John Rushing;Rahul Ramachandran;Udaysankar Nair;Sara Graves;Ron Welch;Hong Lin
Affiliations:
Information Technology and Systems Center, University of Alabama in Huntsville, Technology Hall S327, Huntsville AL 35899, USA;Information Technology and Systems Center, University of Alabama in Huntsville, Technology Hall S327, Huntsville AL 35899, USA;Earth Systems Science Center, University o f Alabama in Huntsville, Huntsville AL 35899, USA;Information Technology and Systems Center, University of Alabama in Huntsville, Technology Hall S327, Huntsville AL 35899, USA;Department of Atmospheric Science, University o f Alabama in Huntsville, Huntsville AL 35899, USA;Information Technology and Systems Center, University of Alabama in Huntsville, Technology Hall S327, Huntsville AL 35899, USA
Venue:
Computers & Geosciences
Year:
2005

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Abstract

Algorithm Development and Mining (ADaM) is a data mining toolkit designed for use with scientific data. It provides classification, clustering and association rule mining methods that are common to many data mining systems. In addition, it provides feature reduction capabilities, image processing, data cleaning and preprocessing capabilities that are of value when mining scientific data. The toolkit is packaged as a suite of independent components, which are designed to work in grid and cluster environments. The toolkit is extensible and scalable, and has been successfully used in several diverse data mining applications. ADaM has also been used in conjunction with other data mining toolkits and with point tools. This paper presents the architecture and design of the ADaM toolkit and discusses its application in detecting cumulus cloud fields in satellite imagery.