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Information Technology and Systems Center The University of Alabama in Huntsville
Huntsville, AL 35899 (256) 824-6868
info@itsc.uah.edu

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Mining Approaches

The Data Mining Solutions Center is working on various research directions in data mining. Two of these directions are Event-based mining and Relationship-based mining. Three kinds of Relationship-based Mining problems are being researched:

  • Coincident Association mining involves market basket analysis to mine for association rules in vector data.
  • Localized Spatial Association mining involves extracting association rules to characterize textures.
  • Temporal Association mining pertains to mining historical trends.

Event-based Mining:

There are three types of event-based mining problems. They are:

Type 1: Known Phenomenon, Known Algorithm

The users know exactly what they are looking for and what sets of algorithms to use. The mining plan is then set up to perform those operations over the data sets of interest. The Mesoscale Convective System (MCS) detection using the SSM/I data set is one such example of known phenomenon/known algorithm mining performed at ITSC.

Type 2: Known Phenomenon, Learned Algorithm

This is known as an algorithm development approach where the end user knows what phenomenon to target but is unsure of the characteristics of the phenomenon or what sequence of algorithms to apply. This is iterative in nature and allows end users the flexibility of fine tuning their algorithm for the event of interest. Cloud mask detection using GOES-8 data is an example of known event, learned algorithm.

Type 3: Search for Anomalies

This category is called target independent data mining. The user searches the data sets for transient events with thresholds. The main idea is to mine for trends depicting anomalies in the data sets. This approach could be used to check the quality of the data set by searching for spurious values or just mining for rare phenomena which would be hard to detect because of the size of the data set. Hinke et al (1997) describe such an application of target independent data mining and the impact in data reduction while retaining all the transient phenomena information.