Intelligence Semantics

New PDF release: Action Rules Mining

By Agnieszka Dardzinska (auth.)

ISBN-10: 3642356494

ISBN-13: 9783642356490

ISBN-10: 3642356508

ISBN-13: 9783642356506

We are surrounded through facts, numerical, express and differently, which needs to to be analyzed and processed to transform it into info that instructs, solutions or aids figuring out and determination making. information analysts in lots of disciplines similar to enterprise, schooling or drugs, are often requested to investigate new information units that are frequently composed of various tables owning various homes. they fight to discover thoroughly new correlations among attributes and express new chances for users.

Action ideas mining discusses a few of information mining and information discovery ideas after which describe consultant recommendations, equipment and algorithms hooked up with motion. the writer introduces the formal definition of motion rule, idea of an easy organization motion rule and a consultant motion rule, the price of organization motion rule, and provides a technique tips on how to build easy organization motion principles of a lowest price. a brand new procedure for producing motion principles from datasets with numerical attributes via incorporating a tree classifier and a pruning step in response to meta-actions can also be awarded. during this booklet we will locate basic thoughts worthy for designing, utilizing and imposing motion ideas besides. certain algorithms are supplied with invaluable rationalization and illustrative examples.

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Two rules with a join support 3 propose value b1 for b(x8 ) and two rules with a join support 2 propose value b2 for b(x8 ). There are two options to construct L(D), either we rule out all contradicting rules and the null value is not being changed or we accept the value which has the highest support. If we follow the second option, we get bS1 (x8 ) = b1 . Assume now that L(D) contains the following rules extracted from S which define values of attribute c (some rules contradict each other): (b, b1 ) → (c, c1 ) (e, e2 ) → (c, c1 ) (f, f4 ) → (c, c1 ) (g, g1 ) → (c, c1 ) (d, d2 ) ∗ (e, e1 ) → (c, c2 ) support support support support support 2, 1, 1, 2, 1, (b, b2 ) ∗ (d, d2 ) → (c, c2 ) (b, b2 ) ∗ (e, e1 ) → (c, c2 ) (b, b2 ) ∗ (f, f2 ) → (c, c2 ) (b, b2 ) ∗ (g, g3 ) → (c, c2 ) (d, d2 ) ∗ (g, g3 ) → (c, c2 ) support support support support support 1, 1, 1, 1, 1.

1. Expressions (a, a2 → a2 ), (b, b2 → b1 ), (c, c2 → c2 ), (c, c3 → c3 ), (d, H → A) are examples of atomic action terms. 1 Main Assumptions 49 (a, a2 → a2 ) = (a, a2 ), (c, c2 → c2 ) = (c, c2 ), (c, c3 → c3 ) = (c, c3 ) mean that the values respectively a2 , c2 , c3 of attributes a and c remain unchanged, while (b, b2 → b1 ) means that the value of attribute b is changed from b2 to b1 . Expressions r1 = [((a, a2 ) ∗ (b, b2 → b1 )) → (d, H → A)], r2 = [[(c, c2 ) ∗ (b, b2 → b1 )] → (d, H → A)] are the examples of action rules.

Now let us define [Y1 , Y2 ] ∩ [Z1 , Z2 ] as [Y1 ∩ Z1 , Y2 ∩ Z2 ] and assume that NS (t1 ) = [Y1 , Y2 ] and NS (t2 ) = [Z1 , Z2 ]. Then NS (t1 ∗ t2 ) = NS (t1 ) ∩ NS (t2 ). Let r = [t1 → t2 ] be an action rule, where NS (t1 ) = [Y1 , Y2 ], NS (t2 ) = [Z1 , Z2 ]. 7. By the support and confidence of rule r we mean: 1. sup(r) = min{card(Y1 ∩ Z1 ), card(Y2 ∩ Z2 )} 1 ∩Z1 ) 2 ∩Z2 ) · card(Y if card(Y1 ) = 0, card(Y2 ) = 0, 2. conf (r) = card(Y card(Y1 ) card(Y2 ) card(Y1 ∩ Z1 ) = 0, card(Y2 ∩ Z2 ) = 0 3.

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Action Rules Mining by Agnieszka Dardzinska (auth.)


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