Action Rules Mining by Agnieszka Dardzinska (auth.)

By Agnieszka Dardzinska (auth.)

We are surrounded by way of info, 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 reminiscent of enterprise, schooling or medication, are often requested to research new information units that are frequently composed of various tables owning various houses. they struggle to discover thoroughly new correlations among attributes and convey new percentages for users.

Action ideas mining discusses a few of info mining and data discovery ideas after which describe consultant ideas, equipment 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 provides a technique easy methods to build uncomplicated organization motion ideas of a lowest expense. a brand new method for producing motion principles from datasets with numerical attributes via incorporating a tree classifier and a pruning step in response to meta-actions can be awarded. during this ebook we will be able to locate primary innovations worthwhile for designing, utilizing and enforcing motion principles in addition. precise algorithms are supplied with beneficial rationalization and illustrative examples.

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For the values of decision attribute we get: e∗1 = {(x1 , 12 ), (x2 , 1), (x4 , 23 ), (x5 , 1)} e∗2 = {(x1 , 12 ), (x4 , 13 ), (x6 , 13 ), (x7 , 1)} e∗3 = {(x3 , 1), (x6 , 23 ), (x8 , 1)}. The next step is to propose a method for checking a relationship between classification attributes and the decision attribute. For any two sets c∗i = {xi , pi }i∈N , e∗j = {yj , qj }j∈M , where pi > 0 and qj > 0, we propose that: {xi , pi }i∈N ⊆ {yj , qj }j∈M iff the support of the rule 42 2 Information Systems ci → ej is above some threshold value.

There are two null values in S corresponding to attribute g, g(x1 ), g(x9 ). Let us work first on g(x1 ). The following rules can be applied: (b, b1 ) → (g, g1 ) support 2, (c, c1 ) ∗ (f, f1 ) → (g, g1 ) support 1. It means that gS5 (x1 ) = g1 . 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.

Assume now that for any two collections of sets X, Y , we write, X ⊆ Y if (∀x ∈ X)(∀y ∈ Y )(x ⊆ y). Let S = (X, ASt ∪ AF l ∪ {d}) be a decision table and B ⊆ ASt ∪ AF l . We say that attribute d depends on B if CLASSS (B) ⊆ CLASSS (d), where CLASSS (B) is a partition of X generated by B [30]. 8. Assume that attribute d depends on B where B ⊆ ASt ∪AF l . The set B is called d-reduct in S if there is no proper subset C of B such that d depends on C. The concept of d -reduct in S was introduced to induce rules from S describing values of the attribute d depending on minimal subsets of ASt ∪ AF l which preserve the confidence of extracted rules.

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