Agnieszka Dardzinska (auth.)'s Action Rules Mining PDF

By Agnieszka Dardzinska (auth.)

ISBN-10: 3642356494

ISBN-13: 9783642356490

ISBN-10: 3642356508

ISBN-13: 9783642356506

We are surrounded by means of facts, numerical, express and another way, which needs to to be analyzed and processed to transform it into info that instructs, solutions or aids figuring out and selection making. info analysts in lots of disciplines akin to enterprise, schooling or drugs, are usually requested to investigate new information units that are usually composed of diverse tables owning assorted houses. they fight to discover thoroughly new correlations among attributes and exhibit new chances for users.

Action principles mining discusses a few of information mining and information discovery rules after which describe consultant ideas, tools and algorithms attached 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 offers a technique tips to build easy organization motion principles of a lowest fee. a brand new method for producing motion ideas from datasets with numerical attributes by means of incorporating a tree classifier and a pruning step in accordance with meta-actions is additionally awarded. during this publication we will locate basic ideas beneficial for designing, utilizing and imposing motion ideas to boot. distinctive algorithms are supplied with precious rationalization and illustrative examples.

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Extra info for Action Rules Mining

Sample text

Since d2 has higher support, then dS3 (x10 ) = d2 . Assume now that L(D) contains the following rules extracted from S which define values of attribute e (some rules contradict each other): (b, b3 ) → (e, e3 ) support 1, (c, c1 ) ∗ (g, g1 ) → (e, e1 ) support 1, (d, d2 ) ∗ (g, g1 ) → (e, e2 ) support 1. There are two null values in S corresponding to attribute e: e(x6 ), e(x9 ). Let us work first on e(x6 ). The following rules can be applied: (c, c1 ) ∗ (g, g1 ) → (e, e1 ) support 1, It means that eS4 (x6 ) = Ve .

Then we obtain next set: 24 2 Information Systems ((a, a1 ), (b, b2 ))∗ = {x2 , x4 } ⊆ {(d, d2 )}∗ - marked Because the last set in covering {a, b} was marked, the algorithm stopped. Therefore, the certain rules, obtained from marked items, are as follows: (a, a2 ) → (d, d3 ) (b, b1 ) → (d, d1 ) (a, a1 ) ∗ (b, b2 ) → (d, d2 ). Possible rules, which come from non-marked items are: (a, a1 ) → (d, d1 ) with confidence 12 (a, a1 ) → (d, d2 ) with confidence 12 (b, b2 ) → (d, d2 ) with confidence 12 (b, b2 ) → (d, d3 ) with confidence 12 .

Assume that S = (X, A, V ), where V = {Va : a ∈ A} and each a ∈ A is a partial function from X into 2Va − {∅}. In the first step of our algorithm, handling incompleteness in S, all incomplete attributes used in S are identified. An attribute is incomplete if there is an object in S with incomplete information on this attribute. The values of all incomplete attributes in S are treated as concepts to be learned (in a form of rules) either only from S or from S and its remote sites (if S is one of collaborating autonomous information systems).

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Action Rules Mining by Agnieszka Dardzinska (auth.)

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