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Multiscale Rough Set Data Analysis (MSRSDA) combines the feature selection and feature extraction ability of RSDA with the multiresolution property of the wavelet transform. RSDA removes the irrelevant (having no impact on prediction performance) and dependent (having no discriminatory power)...
This paper presents a new method to convert continuous variables into discrete variables for inductive machine learning. The method can be applied to pattern classification problems in machine learning and data mining. The discretization process is formulated as an optimization problem. We first...
The Self-Organizing Map (SOM) is a powerful tool for exploratory data analysis which has been employed in a wide range of data mining applications. We present a novel approach to reveal the inherent hierarchical structure of data using multiple SOMs together with heuristics which optimize the...
Most unsupervised learning algorithms ignore prior application knowledge. Also, Self Orgnanized Maps (SOM) based approaches usually develop topographic maps with disjoint and uniform activation regions that correspond to a hard clustering of the patterns at their nodes. We present a novel...
This study presents an application using both single and multiple interval prediction models implemented with artificial neural networks to estimate the future production performance of oil wells. The single interval prediction model was developed using NOL (Gensym Corp., USA). The multiple...
We present, MaxDomino, an algorithm for mining Maximal Frequent Sets (MFS) for discovering association rules in dense databases. The algorithm uses novel concepts of dominancy factor and collapsibility of transaction for efficiently mining MFS. Unlike traditional bottom up approach with...
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