By Agnieszka Dardzinska

We're surrounded via facts, numerical, specific and another way, which needs to to be analyzed and processed to transform it into info that instructs, solutions or aids knowing and choice making. info analysts in lots of disciplines resembling company, schooling or drugs, are often requested to investigate new facts units that are frequently composed of diverse tables owning various homes. they struggle to discover thoroughly new correlations among attributes and exhibit new probabilities for users.

Action principles mining discusses a few of facts mining and information discovery ideas after which describe consultant recommendations, equipment 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 offers a method tips on how to build basic organization motion principles of a lowest expense. a brand new process for producing motion principles from datasets with numerical attributes by means of incorporating a tree classifier and a pruning step in accordance with meta-actions can also be awarded. during this publication we will locate primary suggestions helpful for designing, utilizing and enforcing motion ideas to boot. specific algorithms are supplied with important rationalization and illustrative examples.

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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.

So, gS5 (x9 ) = Vg . 21. The whole process is repeated till no new chased values are identified, which means the procedure Chase1 reaches a fix point. 3 Handling Incomplete Values Using CHASE2 Algorithm Using Chase1 algorithm for predicting what attribute value should replace an incomplete value has a clear advantage over any other method for predicting incomplete values, mainly because of the use of existing associations between values of attributes. 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].

One way to approach this problem is to assign a cost function to all changes of attribute values [64]. If changes of attribute values in the classification part of an action rule are too complex, then they can be replaced by composing such rule with other action rules, as proposed in [63]. Each composition of these rules uniquely defines a new action rule. Objects supporting each new action rule are the same as objects supporting the action rule replaced by it, but the cost of reclassifying them is lower for the new rule.

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