From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Mining Geophysical Data for Knowledge
IEEE Expert: Intelligent Systems and Their Applications
For Scientific Data Discovery: Why Can't the Archive be More Like the Web?
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
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The paper presents a set of requirements for a datamining system for mining remotely sensed satellitedata based on a number of taxonomies that characterizemining of such data. The first of these taxonomies isbased on knowledge of the mining objectives and miningalgorithms. The second is based on variousrelationships that are found in data, including thosebetween different types of data, different spatiallocations of the data and different times of datacapture. The paper then describes the ADaM data miningsystem, which was developed to address theserequirements. The paper describes several data miningtechniques that have been applied to remotely senseddata. The first type is target independent mining,which mines data for transients and trends, with minedresults representing a highly concentrated form of theoriginal data. The second type is the mining ofvectors (representing multi-spectral or fused data)for association rules representing relationshipsbetween the various types of data represented by theelements of the vector. The third type mines data forassociation rules that characterize the texture of thedata.