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

February 2, 2018 | Data Mining | By admin | 0 Comments

By Agnieszka Dardzinska

ISBN-10: 3642356508

ISBN-13: 9783642356506

We're surrounded by way of facts, numerical, express 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. information analysts in lots of disciplines akin to enterprise, schooling or drugs, are often requested to investigate new information units that are frequently composed of various tables owning assorted houses. they fight to discover thoroughly new correlations among attributes and exhibit new probabilities for users.

Action ideas mining discusses a few of information mining and information discovery rules after which describe consultant suggestions, 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 offers a method the right way to build easy organization motion principles of a lowest expense. 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 response to meta-actions can be offered. during this e-book we will be able to locate primary techniques beneficial for designing, utilizing and enforcing motion principles in addition. distinctive algorithms are supplied with priceless clarification and illustrative examples.

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

Sample text

It is domain-independent and requires minimal input from the users, other than to specify a threshold for filtering low-quality patterns [59]. Subjective interestingness measures include actionability [1] and unexpectedness [57]. When a rule contradicts the user’s prior belief about the domain, surprises him or uncovers new knowledge, it is classified as unexpected. 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.

1 Main Assumptions 49 (a, a2 → a2 ) = (a, a2 ), (c, c2 → c2 ) = (c, c2 ), (c, c3 → c3 ) = (c, c3 ) mean that the values respectively a2 , c2 , c3 of attributes a and c remain unchanged, while (b, b2 → b1 ) means that the value of attribute b is changed from b2 to b1 . Expressions r1 = [((a, a2 ) ∗ (b, b2 → b1 )) → (d, H → A)], r2 = [[(c, c2 ) ∗ (b, b2 → b1 )] → (d, H → A)] are the examples of action rules. The rule r1 says that if the value a2 remains unchanged and value b will change from b2 to b1 for a given object x, then it is expected that the value d will change from H to A for object x.

Therefore, to find a covering B of C in D three conditions must occur: 1. Bmin , set B must be minimal, 2. B ⊆ D, set B must be a subset of set D, 3. B → C, set C depends on set B. 1 for each set B, a new indiscernibility relation associated with B must be computed. It can be presented as B = b∈B {b} . 2 for each set B, a new partition generated by B must be determined. The partition of X generated by B may be expressed as B ∗ = b∈B {b}∗ . 2 2 Information Systems System LERS The learning system LERS (Learning from Examples based on Rough Sets) induces a set of rules from examples (any kind of data) and classifies new examples using the set of rules induced previously by LERS [13], [14].

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


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