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ADtrees for Fast Counting and for Fast Learning of Association Rules
2018The problem of discovering association rules in large databases has received considerable research attention. Much research has examined the exhaustive discovery of all association rules involving positive binary literals (e.g. Agrawal et al. 1996). Other research has concerned finding complex association rules for high-arity attributes such as CN2 ...
Brigham S. Anderson +1 more
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Improving Leung's bidirectional learning rule for associative memories
IEEE Transactions on Neural Networks, 2001Leung (1994) introduced a perceptron-like learning rule to enhance the recall performance of bidirectional associative memories (BAMs). He proved that his so-called bidirectional learning scheme always yields a solution within a finite number of learning iterations in case that a solution exists.
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Improving learning rule for fuzzy associative memory with combination of content and association
Neurocomputing, 2015FAM is an associative memory that uses operators of fuzzy logic and mathematical morphology (MM). FAMs possess important advantages including noise tolerance, unlimited storage, and one pass convergence. An important property, deciding FAM performance, is the ability to capture content of each pattern, and association of patterns. Existing FAMs capture
The Duy Bui +2 more
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Multiagent Association Rules Mining in Cooperative Learning Systems
2005Recently, multiagent systems and data mining have attracted considerable attention in the computer science community. This paper combines these two hot research areas to introduce the term multiagent association rule mining on a cooperative learning system, which investigates employing data mining on a cooperative multiagent system.
Reda Alhajj, Mehmet Kaya
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Exploiting association and correlation rules parameters for learning Bayesian networks
Intelligent Data Analysis, 2009In data mining, association and correlation rules are inferred from data in order to highlight statistical dependencies among attributes. The metrics defined for evaluating these rules can be exploited to score relationships between attributes in Bayesian network learning.
STORARI, Sergio +2 more
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Association Rules and Cosine Similarities in Ontology Relationship Learning
2009Ontology learning is the application of automatic tools to extract ontology concepts and relationships from domain text. Whereas ontology learning tools have been fairly successful in extracting concept candidates, it has proven difficult to detect relationships with the same level of accuracy.
Jon Atle Gulla +2 more
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Semi-Supervised Learning to Support the Exploration of Association Rules
2014In the last years, many approaches for post-processing association rules have been proposed. The automatics are simple to use, but they don’t consider users’ subjectivity. Unlike, the approaches that consider subjectivity need an explicit description of the users’ knowledge and/or interests, requiring a considerable time from the user.
Veronica Oliveira de Carvalho +2 more
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Business Rule Learning with Interactive Selection of Association Rules [PDF]
, 2014 Stanislav Vojír +2 more
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Robust learning rule for bidirectional associative memory
Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), 2005A robust learning rule, called adaptive Ho-Kashyap bidirectional learning (AHKBL), is proposed to enhance the capacity and error correction capability of a bidirectional associative memory (BAM). Also, the sufficient conditions for convergence of AHKBL are discussed.
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