Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Case-based reasoning integrations
AI Magazine
Ubiquitous Recommendation Systems
Computer
Using case-based reasoning to diagnostic screening of children with developmental delay
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Formulating strategies for stakeholder management: a case-based reasoning approach
Expert Systems with Applications: An International Journal
Recommender system for software project planning one application of revised CBR algorithm
Expert Systems with Applications: An International Journal
Combinations of case-based reasoning with other intelligent methods
International Journal of Hybrid Intelligent Systems - CIMA-08
Hybrid model for learner modelling and feedback prioritisation in exploratory learning
International Journal of Hybrid Intelligent Systems - CIMA-08
Solving flexible flow-shop problem with a hybrid genetic algorithm and data mining: A fuzzy approach
Expert Systems with Applications: An International Journal
A recommender mechanism based on case-based reasoning
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Case-based reasoning (CBR) is a paradigm, concept and instinctive mechanism for problem solving. Recently, CBR has been widely integrated with some AI algorithms and applied to various kinds of problems. The ill-defined and unstructured problems are suitably solved by CBR. This research proposes a hybrid CBR mechanism including two stages. In stage I, the genetic algorithm is adopted to improve efficiency of case retrieving process. Compared to traditional CBR, the proposed mechanism could reduce about 14% case evaluations, but still achieved 90% satisfactory results. In stage II, the knowledge discovering and data mining (KDD) processes are implemented to produce the refined information from the retrieved cases. Because these retrieved cases and target problem satisfy similar or even same conditions, the outcome of KDD would be more valuable for reference. In addition to retrieved cases of stage I, the proposed mechanism provides direction and relevant knowledge for decision makers in their decision supporting and revising processes in stage II. The proposed CBR mechanism also deals with efficiency and outcome quality issues of traditional CBR.