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Data Mining and Knowledge Discovery Via Logic-Based Methods: Theory, Algorithms, and Applications
Contributor(s): Triantaphyllou, Evangelos (Author)

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 ISBN: 1441916296     ISBN-13: 9781441916297
Publisher: Springer
OUR PRICE: $161.49  

Binding Type: Hardcover - See All Available Formats & Editions
Published: June 2010
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Additional Information
BISAC Categories:
- Computers | Databases - Data Mining
- Business & Economics | Operations Research
- Mathematics | Logic
Dewey: 006.312
LCCN: 2010928843
Series: Springer Optimization and Its Applications
Physical Information: 1" H x 6.1" W x 9.2" L (1.45 lbs) 350 pages
Features: Illustrated
 
Descriptions, Reviews, Etc.
Publisher Description:
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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