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New PDF release: Action Rules Mining (Studies in Computational Intelligence,

By Agnieszka Dardzinska

We're surrounded via facts, numerical, express and another way, which needs to to be analyzed and processed to transform it into details that instructs, solutions or aids realizing and choice making. info analysts in lots of disciplines similar to company, schooling or drugs, are usually requested to research new facts units that are frequently composed of various tables owning various homes. they struggle to discover thoroughly new correlations among attributes and convey new percentages for users.

Action principles mining discusses a few of info mining and data discovery rules after which describe consultant ideas, equipment and algorithms hooked up with motion. the writer introduces the formal definition of motion rule, inspiration of an easy organization motion rule and a consultant motion rule, the price of organization motion rule, and offers a technique easy methods to build uncomplicated organization motion ideas of a lowest price. a brand new method for producing motion ideas from datasets with numerical attributes by way of incorporating a tree classifier and a pruning step in keeping with meta-actions can be awarded. during this ebook we will locate primary options helpful for designing, utilizing and imposing motion ideas besides. distinct algorithms are supplied with worthwhile clarification and illustrative examples.

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Extra resources for Action Rules Mining (Studies in Computational Intelligence, Volume 468)

Example text

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 . (d, d2 ) ∗ (g, g1 ) → (e, e2 ) support 1. 9 Chase Algorithms 37 Now, let us work on e(x9 ). Only one rule can be applied: (b, b3 ) → (e, e3 ) support 1. It means that eS4 (x9 ) = e3 . Assume now that L(D) contains the following rules extracted from S which define values of attribute g (some rules contradict each other): (b, b1 ) → (g, g1 ) (c, c1 ) ∗ (f, f1 ) → (g, g1 ) support 2, support 1, (b, b3 ) → (g, g3 ) support 1, (c, c1 ) ∗ (f, f2 ) → (g, g1 ) support 1.

15. Assume, that X = {x1 , x2 , x3 , x4 , x5 , x6 }. Let us also assume that {a, b, c} are classification attributes and {d} is a decision attribute. The partitions of X generated by single attributes are: {a}∗ = {{x1 , x2 , x3 , x4 }, {x5 , x6 }} {b}∗ = {{x1 , x3 }, {x2 , x4 , x5 , x6 }} {c}∗ = {{x1 , x3 , x5 , x6 }, {x2 , x4 }} For decision attribute {d} we have: {d}∗ = {{x1 , x3 }, {x2 , x4 }, {x5 , x6 }}. Clearly, none of the above is a subset of {d}∗ (not marked), so we go to next step, which is forming two item sets.

Assume now that L(D) = {(t → vc ) ∈ D : c ∈ In(A)} is a consistent set of rules in S. The algorithm, given below, converts information system S to a new more complete information system Chase1 (S). Algorithm CHASE1 (S, In(A), L(D)) INPUT • • System S = (X, A, V ), Set of incomplete attributes In(A) = {a1 , a2 , . . ak }, 34 2 Information Systems BEGIN j := 1; while j ≤ k do begin Sj := S; for all v ∈ Vaj do while there is x ∈ X and a rule (t → v) ∈ L(D) such that x ∈ NSj (t) and card(aj (x)) = 1 do begin a(x) := v; end j := j + 1; end S := {Sj : 1 ≤ j ≤ k} Chase1 (S, In(A), L(D)) END OUTPUT • System CHASE1 (S) The algorithm Chase1 is chasing information system S, attribute by attribute, changing values of attributes assigned to objects in X only after all incomplete attributes in S are being chased.

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