By Agnieszka Dardzinska

ISBN-10: 3642356508

ISBN-13: 9783642356506

We're surrounded via facts, numerical, express and differently, which needs to to be analyzed and processed to transform it into info that instructs, solutions or aids figuring out and determination making. info analysts in lots of disciplines akin to company, schooling or drugs, are often requested to investigate new info units that are usually composed of various tables owning assorted homes. they struggle to discover thoroughly new correlations among attributes and express new chances for users.

Action ideas mining discusses a few of information mining and information discovery ideas after which describe consultant innovations, equipment and algorithms attached with motion. the writer introduces the formal definition of motion rule, concept of an easy organization motion rule and a consultant motion rule, the price of organization motion rule, and offers a technique the way to build easy organization motion principles of a lowest fee. a brand new procedure for producing motion ideas from datasets with numerical attributes by means of incorporating a tree classifier and a pruning step in accordance with meta-actions can be offered. during this publication we will be able to locate primary suggestions priceless for designing, utilizing and imposing motion principles besides. specific algorithms are supplied with valuable rationalization and illustrative examples.

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

Example text

An objective measure is a data-driven approach for evaluating the quality of association patterns. It is domain-independent and requires minimal input from the users, other than to specify a threshold for filtering low-quality patterns [59]. Subjective interestingness measures include actionability [1] and unexpectedness [57]. When a rule contradicts the user’s prior belief about the domain, surprises him or uncovers new knowledge, it is classified as unexpected. A rule is deemed actionable, if the user can take action to gain an advantage based on this rule.

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)].

17, if the associated relations satisfy functional dependency Hospital W ard → Head, then we can infer that variable n1 must correspond to the constant ”Jones” and that variables n4 and n5 must represent the same value. The chase is an algorithm that transforms tableaux system on the basis of constraints. 19 would be produced. 19 Tableaux System X Nurse Name Hospital Ward Head x1 x2 x3 x4 x5 Mary Ann Betty vE Lucy vd vd n2 surgical surgical Jones Jones n3 n4 n5 The variables in tableaux system can be divided into two categories: • • distinguished variables, one for each attribute (if b is an attribute of interest, then vb is the corresponding distinguished variable), nondistinguished variables (there are countably many of them: n1 , n2 , .

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