Feature Selection for Knowledge Discovery and Data Mining

Name: Feature Selection for Knowledge Discovery and Data Mining
Brand: Springer, Berlin
SKU: 9781461376040
Price: 346.22 EUR
Availability: InStock

Автор: uan Liu, Hiroshi Motoda

Език:

Корици: С меки корици

Издател: Springer, Berlin

Наличност: Външен склад

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346.22 € 677.16 лв

As computer power grows and data collection technologies advance, a plethora of data is generated in...

Информация за книгата

Автор

uan Liu, Hiroshi Motoda

Език

Английски език

Корици

Книга - С меки корици

Издадена

2013

страници

214

EAN

9781461376040

ISBN

1461376041

Enbook ID

05257852

Издател

Springer, Berlin

Теглоt

379

Размери

156 x 236 x 13

Пълно описание

As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com puter generated data should be analyzed by computers; without the aid of computing technologies, it is certain that huge amounts of data collected will not ever be examined, let alone be used to our advantages. Even with today's advanced computer technologies (e. g. , machine learning and data mining sys tems), discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Taking its simplest form, raw data are represented in feature-values. The size of a dataset can be measUJ·ed in two dimensions, number of features (N) and number of instances (P). Both Nand P can be enormously large. This enormity may cause serious problems to many data mining systems. Feature selection is one of the long existing methods that deal with these problems. Its objective is to select a minimal subset of features according to some reasonable criteria so that the original task can be achieved equally well, if not better. By choosing a minimal subset offeatures, irrelevant and redundant features are removed according to the criterion. When N is reduced, the data space shrinks and in a sense, the data set is now a better representative of the whole data population. If necessary, the reduction of N can also give rise to the reduction of P by eliminating duplicates.

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Feature Selection for Knowledge Discovery and Data Mining

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Пълно описание

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