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

By Agnieszka Dardzinska

We're surrounded through info, 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 determination making. info analysts in lots of disciplines similar to enterprise, schooling or medication, are usually requested to investigate new info units that are frequently composed of various tables owning assorted houses. they struggle to discover thoroughly new correlations among attributes and convey new probabilities for users.

Action principles mining discusses a few of information mining and data discovery rules after which describe consultant techniques, equipment and algorithms attached 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 technique find out how to build easy organization motion ideas of a lowest rate. a brand new process for producing motion ideas from datasets with numerical attributes via incorporating a tree classifier and a pruning step according to meta-actions can also be offered. during this booklet we will locate basic strategies important for designing, utilizing and enforcing motion principles to boot. particular algorithms are supplied with important rationalization and illustrative examples.

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

Example text

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. E-action rules mining is a method which helps people in intelligently and automatically way to acquire useful information from data. This information can be turned into actions. The approach gives suggestions about how to change certain attribute values of a given set of objects in order to reclassify them according to a user wish.

I} for all jk ≤ J(ik ) do a[(ik , jk )]ik ∈Ik := a(ik , jk ); for all i, j such that both rules a[(ik , jk )]ik ∈Ik → d(m) and a[i, j] → d(m) are not marked and i ∈ Ik do begin if conf (a[(ik , jk )]ik ∈Ik · a[i, j] → d(m)) ≥ λ;then begin mark(a[(ik , jk )]ik ∈Ik · a[i, j]∗ ⊆ d(m∗ )) = positive; output(a[(ik , jk )]ik ∈Ik · a[i, j] → d(m)); end else begin Ik := Ik ∪ {i}; a[(ik , jk )]ik ∈Ik := a[(ik , jk )]ik ∈Ik · a[i, j]; end; end END 26 2 Information Systems The main reason to provide a new version for LEM 2 was to have an algorithm which can be naturally extended to incomplete information systems.

Attributes Blood Pressure and Size are classification attributes while Cholesterol Level is the decision attribute. Let us give another example showing how close is the relationship between replacing Null values by either incomplete or complete values in an incomplete information system and the way queries are interpreted. 9 Chase Algorithms 33 S Disease), where I is an interpretation of queries (the term Brown is treated here as a query). In this section we are interested in how to use rules extracted from an incomplete information system S to replace its null values by values less incomplete.

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