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

By Agnieszka Dardzinska

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

Action principles mining discusses a few of facts mining and data discovery ideas after which describe consultant recommendations, tools and algorithms attached 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 easy organization motion ideas of a lowest expense. a brand new strategy for producing motion ideas from datasets with numerical attributes via incorporating a tree classifier and a pruning step in accordance with meta-actions is additionally provided. during this e-book we will be able to locate basic recommendations important for designing, utilizing and imposing motion principles to boot. exact algorithms are supplied with invaluable rationalization and illustrative examples.

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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. This process is continued till the fixed point is reached (no changes in S are made by Chase1 ).

We suggest using classification rules for introducing a new method connected with action based on their condition features in order to get a desired effect on their decision feature. When we look the bakery example again, the strategy of action would consist of modifying some condition features in order to improve our understanding of customers behavior and then improve the services. E-action rules are useful in many other fields, including medical diagnosis. g. in children flat foot problem, classification rules can explain the relationships between symptoms and sickness and help to predict the diagnosis of a new patient.

Below we show how to compute these two values and decide if the current attribute values assigned to objects x1 , x6 can be replaced by them. Similar process is applied to all incomplete attributes in S. After all changes of all incomplete attributes are recorded, system S is replaced by and the whole process is recursively repeated till some fix point is reached. )}. We will show that Ψ (e(x1 )) = enew (x1 ), which means that the value e(x1 ) will be changed by CHASE2 . To justify our claim, let us compute enew (x) for x ∈ {x1 , x4 , x6 }.

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