Max Bramer, Miltos Petridis's Research and Development in Intelligent Systems XXXIII: PDF

By Max Bramer, Miltos Petridis

ISBN-10: 3319471740

ISBN-13: 9783319471747

ISBN-10: 3319471759

ISBN-13: 9783319471754

The papers during this quantity are the refereed papers provided at AI-2016, the Thirty-sixth SGAI overseas convention on leading edge concepts and functions of man-made Intelligence, held in Cambridge in December 2016 in either the technical and the appliance streams.

They current new and cutting edge advancements and purposes, divided into technical circulation sections on wisdom Discovery and knowledge Mining, Sentiment research and advice, desktop studying, AI strategies, and normal Language Processing, through program flow sections on AI for drugs and incapacity, criminal legal responsibility and Finance, Telecoms and eLearning, and Genetic Algorithms in motion. the quantity additionally comprises the textual content of brief papers offered as posters on the conference.

This is the thirty-third quantity within the study and improvement in clever platforms sequence, which additionally comprises the twenty-fourth quantity within the Applications and concepts in clever Systems sequence. those sequence are crucial interpreting in the event you desire to sustain thus far with advancements during this vital field.

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Appl. 68(20), 16–21 (2013) 13. : An efficient algorithm for mining association rules in large databases. In: VLDB, pp. 432–444 (1995) 14. : An algorithm for constrained association rule mining in semi-structured data. In: PAKDD, pp. 148–158 (1999) 15. : Query-based association rule mining supporting user perspective. Computing 93, 1–25 (2011) Category-Driven Association Rule Mining 35 16. : Mining association rules with item constraints. In: KDD, pp. 67–73 (1997) 17. : Introduction to Data Mining.

I x } ⊆ I and S = {i x+1 , . . , i y } ⊆ I. Moreover, let the mapping M : I → O hold and let C p ⊆ C be a subset of the categories imposed on O. Category-Driven Association Rule Mining 27 1. r is said to adhere to a head-inclusion constraint on C p if all the items in its antecedent map to concepts in O which are associated with a category which falls within C p . ∀i ∈ A(r ) : M(i) = o ∧ σ −1 (o) ⊂ C p 2. r is said to adhere to a head-exclusion constraint on C p if none of the items in its antecedent map to concepts in O which are associated with a category which falls within C p .

B(Φ) = 1. We note M (Φ) the set of models of Φ. SAT problem consists in deciding if a given formula admits a model or not. Let us informally describe the most important components of modern SAT solvers. They are based on a reincarnation of the historical Davis, Putnam, Logemann and Loveland procedure, commonly called DPLL [7]. It performs a backtrack search; selecting at each level of the search tree, a decision variable which is set to a Boolean value. This assignment is followed by an inference step that deduces and propagates some forced unit literal assignments.

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Research and Development in Intelligent Systems XXXIII: Incorporating Applications and Innovations in Intelligent Systems XXIV by Max Bramer, Miltos Petridis


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