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Data Mining and Knowledge Discovery via Logic-Based Methods - Theory, Algorithms, and Applications (Hardcover, 2010 Ed.)
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Data Mining and Knowledge Discovery via Logic-Based Methods - Theory, Algorithms, and Applications (Hardcover, 2010 Ed.)
Series: Springer Optimization and Its Applications, 43
Expected to ship within 12 - 17 working days
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The importance of having ef cient and effective methods for data
mining and kn- ledge discovery (DM&KD), to which the present
book is devoted, grows every day and numerous such methods have
been developed in recent decades. There exists a great variety of
different settings for the main problem studied by data mining and
knowledge discovery, and it seems that a very popular one is
formulated in terms of binary attributes. In this setting, states
of nature of the application area under consideration are described
by Boolean vectors de ned on some attributes. That is, by data
points de ned in the Boolean space of the attributes. It is
postulated that there exists a partition of this space into two
classes, which should be inferred as patterns on the attributes
when only several data points are known, the so-called positive and
negative training examples. The main problem in DM&KD is de ned
as nding rules for recognizing (cl- sifying) new data points of
unknown class, i. e. , deciding which of them are positive and
which are negative. In other words, to infer the binary value of
one more attribute, called the goal or class attribute. To solve
this problem, some methods have been suggested which construct a
Boolean function separating the two given sets of positive and
negative training data points.
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