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Feature Selection for Knowledge Discovery and Data Mining (Paperback, Softcover reprint of the original 1st ed. 1998)
Loot Price: R9,251
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Feature Selection for Knowledge Discovery and Data Mining (Paperback, Softcover reprint of the original 1st ed. 1998)
Series: The Springer International Series in Engineering and Computer Science, 454
Expected to ship within 10 - 15 working days
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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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