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

By Agnieszka Dardzinska

We're surrounded by way of info, numerical, specific and differently, which needs to to be analyzed and processed to transform it into details that instructs, solutions or aids realizing and determination making. facts analysts in lots of disciplines corresponding to enterprise, schooling or drugs, are often requested to investigate new info units that are frequently composed of various tables owning diverse houses. they struggle to discover thoroughly new correlations among attributes and express new percentages for users.

Action principles mining discusses a few of facts mining and data discovery rules after which describe consultant innovations, tools and algorithms attached with motion. the writer introduces the formal definition of motion rule, concept 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 fee. a brand new strategy for producing motion ideas from datasets with numerical attributes through incorporating a tree classifier and a pruning step in accordance with meta-actions can be awarded. during this e-book we will be able to locate basic recommendations valuable for designing, utilizing and imposing motion ideas in addition. special algorithms are supplied with worthwhile rationalization and illustrative examples.

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

Sample text

Assume now that L(D) contains the following rules extracted from S which define values of attribute c (some rules contradict each other): (b, b1 ) → (c, c1 ) (e, e2 ) → (c, c1 ) (f, f4 ) → (c, c1 ) (g, g1 ) → (c, c1 ) (d, d2 ) ∗ (e, e1 ) → (c, c2 ) support support support support support 2, 1, 1, 2, 1, (b, b2 ) ∗ (d, d2 ) → (c, c2 ) (b, b2 ) ∗ (e, e1 ) → (c, c2 ) (b, b2 ) ∗ (f, f2 ) → (c, c2 ) (b, b2 ) ∗ (g, g3 ) → (c, c2 ) (d, d2 ) ∗ (g, g3 ) → (c, c2 ) support support support support support 1, 1, 1, 1, 1.

Classical methods for discovering them follow algorithms either based on frequent sets (called action sets) and association rules mining [3] or they use algorithms such as LERS or ERID with atomic action sets used as their starting step. 1 Main Assumptions Action rules, introduced by Ras and Wieczorkowska [52] may be utilized by any type of industry maintaining large databases, especially medical, military, education and business. They are constructed from classification rules which suggest ways to re-classify objects, such as patients, students or customers to a desired state.

In the current example we take 1 as the threshold for support and 12 as the threshold of confidence. Assume again that c∗i = {xi , pi }i∈N , e∗j = {yj , qj }j∈M . The algorithm will check first the support of the rule ci → ej . If support is below a threshold value, then the corresponding relationship {xi , pi }i∈N ⊆ {yj , qj }j∈M does not hold and it will be not considered in later steps. Otherwise, the confidence of the rule ci → ej is checked. If that confidence is either above or equals the assumed threshold value, the rule is approved and the corresponding relationship {xi , pi }i∈N ⊆ {yj , qj }j∈M is marked.

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