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Vertical Association Rule Mining

Name: Vertical Association Rule Mining
Brand: VDM Verlag Dr. Müller
SKU: 9783639083019
Price: 1618 CZK
Availability: InStock

From Data Representation to Data Mining

Autor: Imad Rahal

Jazyk:

Vazba: Brožovaná

Vydavatel: VDM Verlag Dr. Müller

Dostupnost: U nakladatele na objednávku

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This work focuses on the data-mining task of §association rule mining which discovers association §r...

Informace o knize

Autor

Imad Rahal

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2008

Stránek

132

EAN

9783639083019

Enbook ID

06817504

Vydavatel

VDM Verlag Dr. Müller

Hmotnost

223

Rozměry

151 x 220 x 15

Kompletní popis

This work focuses on the data-mining task of §association rule mining which discovers association §relationships among items in datasets matching user-§defined measures of interest. We describe an §efficient vertical framework for representing data §and mining frequent itemsets that is based on the P-§tree technology along with other artificial §intelligence techniques, such as set-enumeration §trees and tabu search. With the objective of §handling the mounting needs of many applications, §such as precision agriculture, the proposed §framework is used to produce rules in situations §where the ubiquitous support-based pruning is not §sought. In the context of citation graphs, our §proposed framework operates in a (semi) divide-and-§conquer parallelized fashion, to discover patterns §among subject matters that reveal the evolution §history and any possible future extensions of §subject matters. The same framework is utilized §in an interactive incremental parallel model which §focuses on analyzing genome annotation data for §association rules potentially useful in annotating §new genes, replacing missing values, and validating §old annotations. This book provides a unique and fairly comprehensive treatment of a popular data mining task known as association rule mining. At the heart of this book, a vertical framework (based on the patented P-tree technology along with other well-known artificial intelligence techniques) for data representation and data mining is described. The framework is adapted to environments that require divide and conquer parallel processing or pruning beyond the ubiquitous support-based pruning. Along with the theory, the book highlights the versatility of the presented framework in different application domains ranging from citation analysis to precision agriculture and bioinformatics.

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Vertical Association Rule Mining

From Data Representation to Data Mining

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