Download Action Rules Mining by Agnieszka Dardzinska (auth.) PDF

By Agnieszka Dardzinska (auth.)

We are surrounded via facts, numerical, specific and differently, which needs to to be analyzed and processed to transform it into details that instructs, solutions or aids knowing and selection making. facts analysts in lots of disciplines equivalent to company, schooling or medication, are often requested to research new facts units that are frequently composed of diverse tables owning various houses. they fight to discover thoroughly new correlations among attributes and convey new chances for users.

Action ideas mining discusses a few of info mining and data discovery ideas 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 price. a brand new procedure for producing motion ideas from datasets with numerical attributes by way of incorporating a tree classifier and a pruning step according to meta-actions can be provided. during this ebook we will locate primary techniques invaluable for designing, utilizing and enforcing motion ideas in addition. designated algorithms are supplied with helpful clarification and illustrative examples.

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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. 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.

37. 20. Clearly, attributes {b, c, d, e, g} are incomplete, while the attribute f is complete in system S. The assumption that L(D) is consistent in Chase1 algorithm is only for simplification purpose but we can easily drop this condition. In this case, before any rule r from L(D) is used by Chase1 , it has to be checked if there are no other rules in L(D) which contradict with r. Only then rule r can be used for chasing system S. 9 Chase Algorithms 35 To understand the Chase1 better, let us assume that L(D) contains the following rules extracted from S, which define values of attribute b (some rules contradict each other): (e, e2 ) → (b, b1 ) (g, g2 ) → (b, b2 ) (c, c2 ) → (b, b2 ) (c, c3 ) → (b, b3 ) support support support support 2, 2, 2, 1, (c, c1 ) ∗ (f, f1 ) → (b, b1 ) (g, g3 ) ∗ (d, d2 ) → (b, b2 ) (e, e1 ) ∗ (f, f1 ) → (b, b1 ) (e, e3 ) ∗ (d, d3 ) → (b, b3 ) (f, f2 ) ∗ (d, d2 ) → (b, b2 ) support support support support support 2, 1, 1, 1, 1.

The cardinality of the image d(X) = {di : d(x) = di for some x ∈ U } is called the rank of attribute {d} and is denoted by r(d). Let us observe that the decision d determines the partition CLASSS (d) = {X1 , X2 , , Xr(d) } of the set of objects X, where Xk = d−1 ({di }) for 1 ≤ di ≤ r(d). CLASSS (d) is called the classification of objects in S determined by the decision d. As we mentioned before, objects correspond to patients. Also, we assume that patients in d−1 ({d1 }) are better prognoses for a hospital than patients in d−1 ({d2 }) for any d2 ≤ d1 .

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