Get Action Rules Mining PDF

By Agnieszka Dardzinska (auth.)

ISBN-10: 3642356494

ISBN-13: 9783642356490

ISBN-10: 3642356508

ISBN-13: 9783642356506

We are surrounded via facts, numerical, specific and another way, which needs to to be analyzed and processed to transform it into details that instructs, solutions or aids figuring out and choice making. facts analysts in lots of disciplines comparable to company, schooling or medication, are often requested to investigate new info units that are usually composed of diverse tables owning diverse homes. they struggle to discover thoroughly new correlations among attributes and express new percentages for users.

Action principles mining discusses a few of facts 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, proposal of an easy organization motion rule and a consultant motion rule, the price of organization motion rule, and provides a method find out how to build basic organization motion principles of a lowest expense. a brand new process for producing motion principles from datasets with numerical attributes by way of incorporating a tree classifier and a pruning step according to meta-actions can also be awarded. during this booklet we will be able to locate basic options helpful for designing, utilizing and enforcing motion principles to boot. distinct algorithms are supplied with worthy rationalization and illustrative examples.

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So we can’t go further. Therefore the coverings of C = {d} in A = {a, b, c} are: {a, b}, {a, c} and {b, c}. Now, we can proceed to the next step which is extracting rules from coverings. Let us first consider the covering {a, b} computed in the previous step. From this covering we obtain: (a, a1 )∗ = {x1 , x2 , x3 , x4 } (a, a2 )∗ = {x5 , x6 } ⊆ {(d, d3 )}∗ - marked (b, b1 )∗ = {x1 , x3 } ⊆ {(d, d1 )}∗ - marked (b, b2 )∗ = {x2 , x4 , x5 , x6 } Remaining (not marked) sets are (a, a1 )∗ and (b, b2 )∗ , so next step is to concatenate them.

In each of these six cases, a new attribute value assigned to an object in S2 is less general than in S1 . It means that Ψ (S1 ) = S2 . Assume now that L(D) = {(t → vc ) ∈ D : c ∈ In(A)} is the set of all rules extracted from S by ERID(S, λ1 , λ2 ), where λ1 , λ2 are thresholds respectively for minimum support and minimum confidence. 5. The new algorithm, given below, converts information system S of type λ to a new, more complete information system CHASE2 (S). 40 2 Information Systems Algorithm CHASE2 (S, In(A), L(D)) INPUT • • • System S = (X, A, V ), Set of incomplete attributes In(A) = {a1 , a2 , .

In a similar way we justify property a1 for object x5 (with confidence 23 ) So, as far as values of classification attributes, we get: a∗1 = {(x1 , 13 ), (x3 , 1), (x5 , 23 )} a∗2 = {(x1 , 23 ), (x2 , 14 ), (x5 , 13 ), (x6 , 1), (x7 , 1)} a∗3 = {(x2 , 34 ), (x4 , 1), (x8 , 1)} b∗1 = {(x1 , 23 ), (x2 , 13 ), (x4 , 12 ), (x5 , 1), (x7 , 14 )} b∗2 = {(x1 , 13 ), (x2 , 23 ), (x3 , 1), (x4 , 12 ), (x6 , 1), (x7 , 34 ), (x8 , 1)} c∗1 = {(x1 , 1), (x2 , 13 ), (x3 , 12 ), (x7 , 13 ), (x8 , 1)} c∗2 = {(x2 , 13 ), (x4 , 1), (x5 , 1, (x7 , 23 )} c∗3 = {(x2 , 13 ), (x3 , 12 ), (x6 , 1)} d∗1 = {(x1 , 1), (x4 , 1), (x5 , 12 ), (x8 , 1)} d∗2 = {(x2 , 1), (x3 , 1), (x5 , 12 ), (x6 , 1), (x7 , 1)}.

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Action Rules Mining by Agnieszka Dardzinska (auth.)


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