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Read e-book online Action Rules Mining PDF

By Agnieszka Dardzinska (auth.)

ISBN-10: 3642356494

ISBN-13: 9783642356490

ISBN-10: 3642356508

ISBN-13: 9783642356506

We are surrounded through info, 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 knowing and choice making. info analysts in lots of disciplines akin to enterprise, schooling or medication, are usually requested to investigate new information units that are usually composed of various tables owning various homes. they fight to discover thoroughly new correlations among attributes and express new percentages for users.

Action principles mining discusses a few of facts mining and information discovery ideas after which describe consultant thoughts, equipment and algorithms hooked up with motion. the writer introduces the formal definition of motion rule, idea of an easy organization motion rule and a consultant motion rule, the price of organization motion rule, and provides a technique how you can build basic organization motion principles of a lowest rate. a brand new method for producing motion principles from datasets with numerical attributes via incorporating a tree classifier and a pruning step in accordance with meta-actions can also be offered. during this ebook we will locate basic innovations valuable for designing, utilizing and imposing motion principles in addition. exact algorithms are supplied with precious rationalization and illustrative examples.

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If t1 = (a, a1 → a2 ) ∗ t and NS (t) = [Y1 , Y2 ], then NS (t1 ) = [Y1 ∩ {x ∈ X : a(x) = a1 }, Y2 ∩ {x ∈ X : a(x) = a2 }]. Now let us define [Y1 , Y2 ] ∩ [Z1 , Z2 ] as [Y1 ∩ Z1 , Y2 ∩ Z2 ] and assume that NS (t1 ) = [Y1 , Y2 ] and NS (t2 ) = [Z1 , Z2 ]. Then NS (t1 ∗ t2 ) = NS (t1 ) ∩ NS (t2 ). 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.

Object x3 has property a1 with confidence 1 for sure. The confidence that x1 has property a1 is 13 , since (a1 , 13 ) ∈ a(x1 ). In a similar way we justify property a1 for object x5 (with confidence 23 ) So, as far as values of classification attributes, we get: a∗1 = {(x1 , 13 ), (x3 , 1), (x5 , 23 )} a∗2 = {(x1 , 23 ), (x2 , 14 ), (x5 , 13 ), (x6 , 1), (x7 , 1)} a∗3 = {(x2 , 34 ), (x4 , 1), (x8 , 1)} b∗1 = {(x1 , 23 ), (x2 , 13 ), (x4 , 12 ), (x5 , 1), (x7 , 14 )} b∗2 = {(x1 , 13 ), (x2 , 23 ), (x3 , 1), (x4 , 12 ), (x6 , 1), (x7 , 34 ), (x8 , 1)} c∗1 = {(x1 , 1), (x2 , 13 ), (x3 , 12 ), (x7 , 13 ), (x8 , 1)} c∗2 = {(x2 , 13 ), (x4 , 1), (x5 , 1, (x7 , 23 )} c∗3 = {(x2 , 13 ), (x3 , 12 ), (x6 , 1)} d∗1 = {(x1 , 1), (x4 , 1), (x5 , 12 ), (x8 , 1)} d∗2 = {(x2 , 1), (x3 , 1), (x5 , 12 ), (x6 , 1), (x7 , 1)}.

It is based on pairing certain classification rules which have to be discovered first by using for instance algorithms such as LERS [13] or ERID [5], [7]. The tightly coupled framework is often called object-based and it assumes that action rules are discovered directly from a database [6], [16], [18]. Classical methods for discovering them follow algorithms either based on frequent sets (called action sets) and association rules mining [3] or they use algorithms such as LERS or ERID with atomic action sets used as their starting step.

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Action Rules Mining by Agnieszka Dardzinska (auth.)


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