Action Rules Mining by Agnieszka Dardzinska (auth.)

By Agnieszka Dardzinska (auth.)

We are surrounded through info, numerical, express and in a different way, which needs to to be analyzed and processed to transform it into details that instructs, solutions or aids realizing and selection making. facts analysts in lots of disciplines akin to company, schooling or medication, are usually requested to investigate new facts units that are usually composed of various tables owning various houses. they fight to discover thoroughly new correlations among attributes and exhibit new percentages for users.

Action principles mining discusses a few of info mining and information discovery rules after which describe consultant options, tools and algorithms hooked up with motion. the writer introduces the formal definition of motion rule, thought of an easy organization motion rule and a consultant motion rule, the price of organization motion rule, and offers a technique how one can build uncomplicated organization motion principles of a lowest fee. a brand new strategy for producing motion principles from datasets with numerical attributes through incorporating a tree classifier and a pruning step in line with meta-actions can be awarded. during this booklet we will be able to locate basic options worthwhile for designing, utilizing and enforcing motion ideas in addition. special algorithms are supplied with important rationalization and illustrative examples.

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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 , .

Remaining sets are (a, a1 )∗ and (c, c1 )∗ , so next step is to make a pair from them. Then we obtain next set: ((a, a1 ), (c, c1 ))∗ = {x1 , x3 } ⊆ {(d, d1 )}∗ - marked Because the last set in covering {a, c} was marked, the algorithm stopped. The certain rules, obtained from marked items, are as follows: (a, a2 ) → (d, d3 ) (c, c2 ) → (d, d2 ) (a, a1 ) ∗ (c, c1 ) → (d, d1 ). Possible rules, which come from non-marked items are: (a, a1 ) → (d, d1 ) with confidence 12 (a, a1 ) → (d, d2 ) with confidence 12 (c, c1 ) → (d, d1 ) with confidence 12 (c, c1 ) → (d, d3 ) with confidence 12 .

Standard interpretation NS of action terms in S = (X, A, V ) is defined as follow: 1. If (a, a1 → a2 ) is an atomic term, then NS ((a, a1 → a2 )) = [{x ∈ X : a(x) = a1 }, {x ∈ X : a(x) = a2 }] 2. If t1 = (a, a1 → a2 ) ∗ t and NS (t) = [Y1 , Y2 ], then NS (t1 ) = [Y1 ∩ {x ∈ X : a(x) = a1 }, Y2 ∩ {x ∈ X : a(x) = a2 }]. 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 ].

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