By Agnieszka Dardzinska (auth.)

ISBN-10: 3642356494

ISBN-13: 9783642356490

ISBN-10: 3642356508

ISBN-13: 9783642356506

We are surrounded through facts, numerical, express and in a different way, which needs to to be analyzed and processed to transform it into details that instructs, solutions or aids figuring out and choice making. info analysts in lots of disciplines similar to enterprise, schooling or medication, are usually requested to research new facts units that are frequently composed of various tables owning various homes. they struggle to discover thoroughly new correlations among attributes and exhibit new chances for users.

Action principles mining discusses a few of info mining and data discovery ideas after which describe consultant strategies, tools 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 offers a method the best way to build easy organization motion ideas of a lowest fee. a brand new method for producing motion ideas from datasets with numerical attributes through incorporating a tree classifier and a pruning step in accordance with meta-actions is usually provided. during this ebook we will locate primary suggestions precious for designing, utilizing and imposing motion principles besides. unique algorithms are supplied with important clarification and illustrative examples.

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Extra resources for Action Rules Mining

Sample text

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. Domain experts basically look at a rule and say that this rule can be converted into an appropriate action.

Xik } : (∀j ≤ k)[xij ∈ D], where k is a positive integer. 8 Extracting Classification Rules 23 then add B to K; k := k + 1 end END Below, there is an example showing how to find covering using LEM 1 module of LERS. 31. 15. Assume, that X = {x1 , x2 , x3 , x4 , x5 , x6 }. Let us also assume that {a, b, c} are classification attributes and {d} is a decision attribute. The partitions of X generated by single attributes are: {a}∗ = {{x1 , x2 , x3 , x4 }, {x5 , x6 }} {b}∗ = {{x1 , x3 }, {x2 , x4 , x5 , x6 }} {c}∗ = {{x1 , x3 , x5 , x6 }, {x2 , x4 }} For decision attribute {d} we have: {d}∗ = {{x1 , x3 }, {x2 , x4 }, {x5 , x6 }}.

Xik } : (∀j ≤ k)[xij ∈ D], where k is a positive integer. 8 Extracting Classification Rules 23 then add B to K; k := k + 1 end END Below, there is an example showing how to find covering using LEM 1 module of LERS. 31. 15. Assume, that X = {x1 , x2 , x3 , x4 , x5 , x6 }. Let us also assume that {a, b, c} are classification attributes and {d} is a decision attribute. The partitions of X generated by single attributes are: {a}∗ = {{x1 , x2 , x3 , x4 }, {x5 , x6 }} {b}∗ = {{x1 , x3 }, {x2 , x4 , x5 , x6 }} {c}∗ = {{x1 , x3 , x5 , x6 }, {x2 , x4 }} For decision attribute {d} we have: {d}∗ = {{x1 , x3 }, {x2 , x4 }, {x5 , x6 }}.

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Action Rules Mining by Agnieszka Dardzinska (auth.)


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