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Association rule learning
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231	TITLE 610. STATE REGENTS FOR HIGHER EDUCATION CHAPTER 25. STUDENT FINANCIAL AID AND SCHOLARSHIPS SUBCHAPTER 23. OKLAHOMA HIGHER LEARNING ACCESS PROGRAM AGENCY RULE REPORT April 22, 2013 Add to Reading List Source URL: www.okhighered.org Language: English - Date: 2014-02-18 23:22:37 Association of Public and Land-Grant Universities American Association of State Colleges and Universities Oklahoma State Regents for Higher Education Grade Oklahoma Rulemaking United States Environmental Protection Agency Oklahoma State System of Higher Education Education Evaluation Academia
232	A Hybrid Diagnosis Approach Combining Black-Box and White-Box Reasoning Mingmin Chen1 , Shizhuo Yu1 , Nico Franz2 , Shawn Bowers3 , and Bertram Lud¨ascher1 1 Dept. of Computer Science, University of California, Davis, Add to Reading List Source URL: taxonbytes.org Language: English - Date: 2014-07-01 16:19:30 Computing Computer programming Programming paradigms Constraint satisfaction problem Answer set programming Algorithm Association rule learning Datalog Constraint programming Software engineering Logic programming
233	LISp-Miner project: http://lispminer.vse.cz Demonstration overview Demonstration of the LISp-Miner system covers the following: • LISp-Miner application to your own data. We recommend to use the foll Add to Reading List Source URL: lispminer.vse.cz Language: English - Date: 2004-10-29 14:15:02 Data analysis Lisp programming language Functional languages Source code Lisp Data mining Association rule learning Contingency table Subroutine Computer programming Software engineering Computing
234	Chapter 6 Frequent Itemsets We turn in this chapter to one of the major families of techniques for characterizing data: the discovery of frequent itemsets. This problem is often viewed as the discovery of “association Add to Reading List Source URL: infolab.stanford.edu Language: English - Date: 2012-07-04 16:03:18 Apriori algorithm Information Market basket Stack Basket Recall Algorithm Hash table Data mining Association rule learning Information science
235	Index Bandwidth, 20 Basket, see Market basket, 184, 186, 187, 216 Bayes net, 4 BDMO Algorithm, 251 Add to Reading List Source URL: infolab.stanford.edu Language: English - Date: 2012-07-04 16:04:10 Data mining Information retrieval Searching Matrix theory Sparse matrices Hierarchical clustering MinHash Matrix Association rule learning Mathematics Algebra Information science
236	IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 36, NO. 3, JUNE[removed]Fuzzy Versus Quantitative Association Rules: A Fair Data-Driven Comparison Hannes Verlinde, Martine De Cock, and Raymon Add to Reading List Source URL: www.cwi.ugent.be Language: English - Date: 2006-06-30 12:00:01 Association rule learning Data mining Fuzzy set Membership function Type-2 fuzzy sets and systems Fuzzy logic Logic Mathematical logic
237	FUZZY ASSOCIATION RULES: A TWO-SIDED APPROACH M. De Cock C. Cornelis E. E. Kerre Dept. of Applied Mathematics and Computer Science Ghent University, Krijgslaan 281 (S9), B-9000 Gent, Belgium phone: +[removed], fax: Add to Reading List Source URL: www.fuzzy.ugent.be Language: English - Date: 2005-06-09 09:15:55 Fuzzy logic Measure theory Support Association rule learning Exponentiation Boolean algebra Expected value Rough set Fuzzy mathematics Logic Mathematical logic Mathematics
238	Fuzzy Sets and Systems[removed] – 85 www.elsevier.com/locate/fss Elicitation of fuzzy association rules from positive and negative examples M. De Cock∗ , C. Cornelis, E.E. Kerre Add to Reading List Source URL: www.fuzzy.ugent.be Language: English - Date: 2005-06-09 09:15:55 Data mining Computing Association rule learning Fuzzy control system Software engineering Attribute grammar HTML element EXPRESS Rough set Fuzzy logic Logic Data management
239	Mining Positive and Negative Fuzzy Association Rules Peng Yan1 , Guoqing Chen1 , Chris Cornelis2 , Martine De Cock2 , and Etienne Kerre2 1 Add to Reading List Source URL: www.fuzzy.ugent.be Language: English - Date: 2005-06-09 09:15:57 Logic Data mining Association rule learning Fuzzy logic Tuple Expected value Mathematics Data management Mathematical logic
240	SOFTWARE—PRACTICE AND EXPERIENCE Softw. Pract. Exper. 2010; 00:1–7 Prepared using speauth.cls [Version: [removed]v2.2] Sharing Experiments Using Add to Reading List Source URL: www.greggay.com Language: English - Date: 2010-07-07 12:25:52 Data analysis Data mining Weka Machine learning AWK Statistical hypothesis testing Association rule learning Science Information Statistics

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