Read e-book online Action Rules Mining (Studies in Computational Intelligence, PDF

By Agnieszka Dardzinska

ISBN-10: 3642356508

ISBN-13: 9783642356506

We're surrounded via info, numerical, specific and another way, which needs to to be analyzed and processed to transform it into info that instructs, solutions or aids realizing and determination making. facts analysts in lots of disciplines comparable to company, schooling or medication, are usually requested to investigate new facts units that are usually composed of diverse tables owning various houses. they struggle to discover thoroughly new correlations among attributes and exhibit new percentages for users.

Action ideas mining discusses a few of facts mining and data discovery rules after which describe consultant options, tools and algorithms hooked up with motion. the writer introduces the formal definition of motion rule, inspiration of an easy organization motion rule and a consultant motion rule, the price of organization motion rule, and provides a technique tips to build easy organization motion ideas of a lowest rate. a brand new strategy for producing motion ideas from datasets with numerical attributes via incorporating a tree classifier and a pruning step according to meta-actions can be awarded. during this booklet we will locate primary thoughts important for designing, utilizing and enforcing motion ideas besides. particular algorithms are supplied with worthwhile clarification and illustrative examples.

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5 ) (c1 , d2 )∗ ⊆ e∗3 (sup = 12 < 1) - marked positive - marked negative All sets were marked, therefore we can already start to extract rules from an incomplete information system. 5 Only two values e(x1 ), e(x6 ) of the attribute e can be changed. Below we show how to compute these two values and decide if the current attribute values assigned to objects x1 , x6 can be replaced by them. Similar process is applied to all incomplete attributes in S. After all changes of all incomplete attributes are recorded, system S is replaced by and the whole process is recursively repeated till some fix point is reached.

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.

Clearly, none of the above is a subset of {d}∗ (not marked), so we go to next step, which is forming two item sets. {a, b}∗ = {{x1 , x3 }, {x2 , x4 }, {x5 , x6 }} ⊆ {d}∗ - marked {a, c}∗ = {{x1 , x3 }, {x2 , x4 }, {x5 , x6 }} ⊆ {d}∗ - marked {b, c}∗ = {{x1 , x3 }, {x2 , x4 }, {x5 , x6 }} ⊆ {d}∗ - marked All of the above sets are marked. So we can’t go further. Therefore the coverings of C = {d} in A = {a, b, c} are: {a, b}, {a, c} and {b, c}. Now, we can proceed to the next step which is extracting rules from coverings.

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Action Rules Mining (Studies in Computational Intelligence, Volume 468) by Agnieszka Dardzinska


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