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Action Rules Mining by Agnieszka Dardzinska (auth.) PDF

By Agnieszka Dardzinska (auth.)

ISBN-10: 3642356494

ISBN-13: 9783642356490

ISBN-10: 3642356508

ISBN-13: 9783642356506

We are surrounded via info, 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 realizing and determination making. information analysts in lots of disciplines resembling company, schooling or drugs, are often requested to research new facts units that are frequently composed of various tables owning varied houses. they fight to discover thoroughly new correlations among attributes and convey new chances for users.

Action principles mining discusses a few of information mining and data discovery ideas after which describe consultant thoughts, tools and algorithms attached with motion. the writer introduces the formal definition of motion rule, thought of an easy organization motion rule and a consultant motion rule, the price of organization motion rule, and provides a method the best way to build uncomplicated organization motion ideas of a lowest rate. a brand new process for producing motion ideas from datasets with numerical attributes by means of incorporating a tree classifier and a pruning step in keeping with meta-actions is additionally provided. during this e-book we will be able to locate basic suggestions worthy for designing, utilizing and imposing motion ideas to boot. distinct algorithms are supplied with worthy rationalization and illustrative examples.

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Extra info for Action Rules Mining

Sample text

On the basis of these approximations, LERS computes two corresponding sets of rules: certain and possible. One of potential applications is the use of expert systems, equipped with rules induced by LERS, as advisory systems, helping in decision making and improvement strategy [13]. Its input data is represented as a decision table. Examples are described by values of attributes and characterized by a value of a decision. All examples with the same value of the decision belong to the same concept.

To find these associations we can use either any association rule mining algorithm [3] or any rule discovery algorithm like LERS [13] or Rosetta [22]. Unfortunately, these algorithms, including Chase1 , do not handle partially incomplete data, where attribute a(x) can have several possible values, for instance, to {(a1 , 14 ), (a2 , 14 ), (a3 , 12 )}. Clearly, we assume here that a is an attribute, x is an object, and {a1 , a2 , a3 } ⊆ Va . The weights assigned to these three attribute values should be read as: • • • the confidence that a(x) = a1 is 14 , the confidence that a(x) = a2 is 14 , the confidence that a(x) = a3 is 12 .

The effect of algorithm will be explained with the example from medical database. Let us assume we have a decision system, with only one decision attribute, seen as method of treatment. Its domain contains values being integers. This 52 3 Action Rules decision attribute classifies objects (patients) with respect to the prognoses for patients. The cardinality of the image d(X) = {di : d(x) = di for some x ∈ U } is called the rank of attribute {d} and is denoted by r(d). Let us observe that the decision d determines the partition CLASSS (d) = {X1 , X2 , , Xr(d) } of the set of objects X, where Xk = d−1 ({di }) for 1 ≤ di ≤ r(d).

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Action Rules Mining by Agnieszka Dardzinska (auth.)


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