An Investigation into the use of a Neural Tree Classifier for Knowledge Discovery in OLAP databases

eBook (PDF), 152 Pages
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Modern OLAP platforms are capable of creating databases terabytes in size and present a significant challenge to the analyst with the goal of knowledge discovery. Artificial neural networks represent an aspect of machine learning that offers promise in this area. A neural map can learn to identify patterns in data of high dimensionality and a specific type of neural map, a neural tree classifier, can provide a hierarchical classification of the patterns identified. The investigation begins with a comparison of two neural tree classifiers and continues by illustrating how their application can allow the identification of multi-dimensional areas of analytical interest in an OLAP database. Finally, a novel OLAP exception "explain" technique is outlined, enabled through the use of a neural tree classifier in conjunction with discovery-driven exploration.
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Product Details

September 30, 2011
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2.46 MB
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Required Software Any PDF Reader, Apple Preview
Supported Devices Windows PC/PocketPC, Mac OS, Linux OS, Apple iPhone/iPod Touch... (See More)
# of Devices Unlimited
Flowing Text / Pages Pages
Printable? Yes
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