By Agnieszka Dardzinska

We're surrounded by means of info, numerical, specific and differently, which needs to to be analyzed and processed to transform it into info that instructs, solutions or aids knowing and determination making. facts analysts in lots of disciplines reminiscent of company, schooling or drugs, are often requested to research new info units that are frequently composed of various tables owning diverse houses. they struggle to discover thoroughly new correlations among attributes and express new probabilities for users.

Action ideas mining discusses a few of information mining and data discovery rules after which describe consultant options, 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 tips on how to build uncomplicated organization motion ideas of a lowest expense. a brand new method for producing motion principles from datasets with numerical attributes by means of incorporating a tree classifier and a pruning step according to meta-actions can be offered. during this e-book we will be able to locate basic thoughts important for designing, utilizing and imposing motion principles besides. unique algorithms are supplied with invaluable clarification and illustrative examples.

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

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The following rules can be applied: (b, b1 ) → (c, c1 ) (f, f4 ) → (c, c1 ) support 2, support 1, (e, e2 ) → (c, c1 ) (g, g1 ) → (c, c1 ) support 1, support 2. Since all these rules support the value c1 for c(x7 ), then cS2 (x7 ) = c1 . 36 2 Information Systems Now, algorithm Chase1 will try to change the value c(x8 ). The following rules can be applied: (e, e2 ) → (c, c1 ) support 1, (d, d2 ) ∗ (g, g3 ) → (c, c2 ) support 1. Since both rules have the same support, then the value of c(x8 ) remains unchanged which means cS2 (x8 ) = Vc (undefined).

The second step of the algorithm is to extract rules describing these concepts. These rules are stored in a knowledge base D for information system S (see [35], [36]). The algorithm Chase1 proposed in this section assumes that all inconsistencies in D have to be repaired before they can be used in the chase process. Rules describing attribute value va of attribute a are extracted from the subsystem S1 = (X1 , A, V ) of S where X1 = {x ∈ X : card(a(x)) = 1}. The final step of our procedure is to replace the incomplete information in S by values provided by rules in D.

In the object-based approach, action rules are extracted directly from a database [6], [16], [18] ,while in rule-based approach [52], the extraction of actionable knowledge is a consequence of using classification rules discovery. It is further subdivided into: methods generating action rules from certain pairs of classification rules [50], [51], [63], [66], and methods generating action rules from single classification rules [40], [48]. 2 Action Rules from Classification Rules 51 classification rules and constructs action rules directly from them.

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