Action Rules Mining (Studies in Computational Intelligence, by Agnieszka Dardzinska

By Agnieszka Dardzinska

We're surrounded by means of information, 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 knowing and choice making. information analysts in lots of disciplines reminiscent of enterprise, schooling or drugs, are usually requested to research new information units that are frequently composed of various tables owning assorted homes. they struggle to discover thoroughly new correlations among attributes and exhibit new percentages for users.

Action principles mining discusses a few of facts mining and information discovery ideas after which describe consultant ideas, tools and algorithms hooked up 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 provides a method how one can build basic organization motion ideas of a lowest fee. a brand new process for producing motion principles from datasets with numerical attributes by means of incorporating a tree classifier and a pruning step in line with meta-actions is usually awarded. during this ebook we will locate primary recommendations invaluable for designing, utilizing and enforcing motion ideas besides. particular algorithms are supplied with beneficial clarification and illustrative examples.

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Now, let us work on g(x9 ). The following rules can be applied: (b, b3 ) → (g, g3 ) support 3, (c, c1 ) ∗ (f, f2 ) → (g, g1 ) support 1. So, gS5 (x9 ) = Vg . 21. The whole process is repeated till no new chased values are identified, which means the procedure Chase1 reaches a fix point. 3 Handling Incomplete Values Using CHASE2 Algorithm Using Chase1 algorithm for predicting what attribute value should replace an incomplete value has a clear advantage over any other method for predicting incomplete values, mainly because of the use of existing associations between values of attributes.

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.

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 , . . 2. To define system S it is enough to assume that: aS (x) = (if a = aj then aSj (x)) for any attribute a and object x. Also, if bj (x) = {(vi , pi )}i∈I then [bj (x)/p] = {(vi , pi /p)}i∈I . 23. We only show how values of the attribute e will change.

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