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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

Published
September 30, 2011
Language
English
Pages
152
File Format
PDF
File Size
2.46 MB

Formats for this Ebook

PDF
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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