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

By Agnieszka Dardzinska

We're surrounded through facts, numerical, specific and differently, which needs to to be analyzed and processed to transform it into details that instructs, solutions or aids realizing and choice making. information analysts in lots of disciplines akin to company, schooling or drugs, are usually requested to research new info units that are frequently composed of various tables owning diversified houses. they fight to discover thoroughly new correlations among attributes and express new chances for users.

Action principles mining discusses a few of info mining and information discovery ideas after which describe consultant ideas, tools and algorithms hooked up with motion. the writer introduces the formal definition of motion rule, suggestion of an easy organization motion rule and a consultant motion rule, the price of organization motion rule, and provides a technique the best way to build easy organization motion ideas of a lowest price. a brand new strategy for producing motion ideas from datasets with numerical attributes via incorporating a tree classifier and a pruning step in response to meta-actions is usually offered. during this e-book we will locate basic ideas invaluable for designing, utilizing and enforcing motion principles besides. particular algorithms are supplied with useful rationalization and illustrative examples.

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

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47–89. 1. By an atomic action term we mean an expression (a, a1 → a2 ), where a is an attribute, and {a1 , a2 } ∈ Va . If a1 = a2 then a is called stable on a1 . In this case, for simplicity reason, we use notation (a, a1 ) instead of (a, a1 → a2 ). 2. By a set of action terms we mean the smallest set such that: 1. If t is an atomic action term, then t is an action term. 2. If t1 , t2 are action terms, then t1 ∗ t2 is an action term. 3. If t is an action term containing (a, a1 → a2 ), (b, b1 → b2 ) as its subterms, then a = b.

The following rules can be applied: (b, b1 ) → (c, c1 ) (f, f4 ) → (c, c1 ) support 2, support 1, (e, e2 ) → (c, c1 ) (g, g1 ) → (c, c1 ) support 1, support 2. Since all these rules support the value c1 for c(x7 ), then cS2 (x7 ) = c1 . 36 2 Information Systems Now, algorithm Chase1 will try to change the value c(x8 ). The following rules can be applied: (e, e2 ) → (c, c1 ) support 1, (d, d2 ) ∗ (g, g3 ) → (c, c2 ) support 1. Since both rules have the same support, then the value of c(x8 ) remains unchanged which means cS2 (x8 ) = Vc (undefined).

Possible rules, which come from non-marked items are: (a, a1 ) → (d, d1 ) with confidence 12 (a, a1 ) → (d, d2 ) with confidence 12 (b, b2 ) → (d, d2 ) with confidence 12 (b, b2 ) → (d, d3 ) with confidence 12 . For covering {a, c} we obtain: (a, a1 )∗ = {x1 , x2 , x3 , x4 } (a, a2 )∗ = {x5 , x6 } ⊆ {(d, d3 )}∗ - marked (c, c1 )∗ = {x1 , x3 , x5 , x6 } (c, c2 )∗ = {x2 , x4 } ⊆ {(d, d2 )}∗ - marked. Remaining sets are (a, a1 )∗ and (c, c1 )∗ , so next step is to make a pair from them. Then we obtain next set: ((a, a1 ), (c, c1 ))∗ = {x1 , x3 } ⊆ {(d, d1 )}∗ - marked Because the last set in covering {a, c} was marked, the algorithm stopped.

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