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

By Agnieszka Dardzinska

ISBN-10: 3642356508

ISBN-13: 9783642356506

We're surrounded by means of facts, numerical, specific and in a different way, which needs to to be analyzed and processed to transform it into details that instructs, solutions or aids realizing and choice making. info analysts in lots of disciplines resembling company, schooling or drugs, are often requested to investigate new facts units that are frequently composed of diverse tables owning varied houses. they struggle to discover thoroughly new correlations among attributes and convey new chances for users.

Action ideas mining discusses a few of information 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, inspiration of an easy organization motion rule and a consultant motion rule, the price of organization motion rule, and offers a method tips on how to build easy organization motion principles of a lowest rate. a brand new strategy for producing motion ideas from datasets with numerical attributes by way of incorporating a tree classifier and a pruning step in keeping with meta-actions is usually provided. during this booklet we will be able to locate primary strategies important for designing, utilizing and enforcing motion ideas besides. certain algorithms are supplied with precious rationalization and illustrative examples.

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

Example text

Let r = [t1 → t2 ] be an action rule, where NS (t1 ) = [Y1 , Y2 ], NS (t2 ) = [Z1 , Z2 ]. 7. By the support and confidence of rule r we mean: 1. sup(r) = min{card(Y1 ∩ Z1 ), card(Y2 ∩ Z2 )} 1 ∩Z1 ) 2 ∩Z2 ) · card(Y if card(Y1 ) = 0, card(Y2 ) = 0, 2. conf (r) = card(Y card(Y1 ) card(Y2 ) card(Y1 ∩ Z1 ) = 0, card(Y2 ∩ Z2 ) = 0 3. conf (r) = 0 in other cases The definition of a confidence should be interpreted as an optimistic confidence. 5, we can find many action rules associated with system S. Let us take two rules extracted earlier: r1 = [((a, a2 ) ∗ (b, b2 → b1 )) → (d, H → A)], r2 = [((c, c2 ) ∗ (b, b2 → b1 )) → (d, H → A)].

5 Only two values e(x1 ), e(x6 ) of the attribute e can be changed. 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 .

Expressions r1 = [((a, a2 ) ∗ (b, b2 → b1 )) → (d, H → A)], r2 = [[(c, c2 ) ∗ (b, b2 → b1 )] → (d, H → A)] are the examples of action rules. The rule r1 says that if the value a2 remains unchanged and value b will change from b2 to b1 for a given object x, then it is expected that the value d will change from H to A for object x. Clearly, Dom(r1 ) = {a, b, d}. In a similar way, the rule r2 says that if the value c2 remains unchanged and value b will change from b2 to b1 , then it is expected that the value d will change from H to A, and Dom(r2 ) = {b, c, d}.

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Action Rules Mining (Studies in Computational Intelligence, Volume 468) by Agnieszka Dardzinska


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