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

By Agnieszka Dardzinska

We're surrounded by way of information, numerical, specific and another 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 corresponding to enterprise, schooling or medication, are often requested to investigate new information units that are frequently composed of diverse tables owning diverse homes. they fight to discover thoroughly new correlations among attributes and convey new percentages for users.

Action principles mining discusses a few of info mining and data 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 tips to build uncomplicated organization motion ideas of a lowest rate. 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 is usually offered. during this booklet we will locate primary recommendations precious for designing, utilizing and enforcing motion principles to boot. particular algorithms are supplied with precious clarification and illustrative examples.

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

Example text

24. Here we have X = {x1 , x2 , x3 , x4 , x5 , x6 , x7 , x8 } and A = {a, b, c, d, e}. 3 X Attribute a x1 x2 x3 x4 x5 x6 x7 x8 Attribute b Attribute c Attribute d Attribute e (a1 , 13 ), (a2 , 23 ) (b1 , 23 ), (b2 , 13 ) c1 d1 (a2 , 14 ), (a3 , 34 ) (b1 , 13 ), (b2 , 23 ) d2 a1 b2 (c1 , 12 ), (c3 , 12 ) d2 a3 c2 d1 (a1 , 23 ), (a2 , 13 ) b1 c2 a2 b2 c3 d2 a2 (b1 , 14 ), (b2 , 34 ) (c1 , 13 ), (c2 , 23 ) d2 a3 b2 c1 d1 (e1 , 12 ), (e2 , 12 ) e1 e3 (e1 , 23 ), (e2 , 13 ) e1 (e2 , 13 ), (e3 , 23 ) e2 e3 Let us try to extract rules from S describing attribute e in terms of attributes {a, b, c, d} following a strategy similar to LERS.

32. 16. For simplicity, for all attributes, we will use notation ai instead of (a, ai ). Using LERS-LEM2 algorithm and a minimum confidence threshold: λ = 34 first we create classes related to decision attribute {d}: d∗1 = {x1 , x2 , x5 } d∗2 = {x3 , x4 , x6 } and classes connected with classification attributes. First loop: a∗1 = {x1 , x3 , x4 } a∗2 = {x5 } a∗3 = {x2 , x6 } b∗1 = {x2 , x5 } b∗2 = {x1 , x3 , x6 } c∗1 = {x1 , x4 , x5 } c∗2 = {x3 , x6 } a∗1 ⊆ d∗1 a∗1 ⊆ d∗2 a∗2 ⊆ d∗1 a∗3 ⊆ d∗1 a∗3 ⊆ d∗2 b∗1 ⊆ d∗1 b∗2 ⊆ d∗1 b∗2 ⊆ d∗2 c∗1 ⊆ d∗1 c∗1 ⊆ d∗2 c∗2 ⊆ d∗2 (not marked) (not marked) marked (not marked) (not marked) marked (not marked) (not marked) (not marked) (not marked) marked Second loop: (building terms of length 2 from terms that have not been marked): (a1 , b2 )∗ = {x1 , x3 } (a1 , c1 )∗ = {x1 , x4 } (a3 , b2 )∗ = {x6 } (a3 , c1 )∗ = ∅ (b2 , c1 )∗ = {x1 } (a1 , b2 )∗ (a1 , b2 )∗ (a1 , c1 )∗ (a1 , c1 )∗ (a3 , b2 )∗ ⊆ d∗1 ⊆ d∗2 ⊆ d∗1 ⊆ d∗2 ⊆ d∗2 (b2 , c1 )∗ ⊆ d∗1 (not marked) (not marked) (not marked) (not marked) marked marked, but no rule marked Third loop: (building terms of length 3 from terms of length 2 and length 1 that have not been marked): The set (a1 , b2 , c1 )∗ is not considered as superset of (b2 , c1 )∗ which was previously marked.

If support is below a threshold value, then the corresponding relationship {xi , pi }i∈N ⊆ {yj , qj }j∈M does not hold and it will be not considered in later steps. Otherwise, the confidence of the rule ci → ej is checked. If that confidence is either above or equals the assumed threshold value, the rule is approved and the corresponding relationship {xi , pi }i∈N ⊆ {yj , qj }j∈M is marked. Otherwise this corresponding relationship remains unmarked. 37) - not marked - marked negative - not marked We obtained seven marked positive rules, where support and threshold hold, and nine rules, where only support holds.

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