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Approach to Clustering with Variance-Based XCS
- Source :
- Journal of Advanced Computational Intelligence and Intelligent Informatics. 21:885-894
- Publication Year :
- 2017
- Publisher :
- Fuji Technology Press Ltd., 2017.
-
Abstract
- This paper presents an approach to clustering that extends the variance-based Learning Classifier System (XCS-VR). In real world problems, the ability to combine similar rules is crucial in the knowledge discovery and data mining field. Conventionally, XCS-VR is able to acquire generalized rules, but it cannot further acquire more generalized rules from these rules. The proposed approach (called XCS-VRc) accomplishes this by integrating similar generalized rules. To validate the proposed approach, we designed a bench-mark problem to examine whether XCS-VRc can cluster both the generalized and more generalized features in the input data. The proposed XCS-VRc proved to be more efficient than XCS and the conventional XCS-VR.
- Subjects :
- TheoryofComputation_COMPUTATIONBYABSTRACTDEVICES
Computer science
Conceptual clustering
Multi-task learning
02 engineering and technology
Semi-supervised learning
Machine learning
computer.software_genre
ComputingMethodologies_ARTIFICIALINTELLIGENCE
050105 experimental psychology
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
0501 psychology and cognitive sciences
Cluster analysis
Learning classifier system
business.industry
05 social sciences
Variance (accounting)
Human-Computer Interaction
ComputingMethodologies_PATTERNRECOGNITION
Margin classifier
Unsupervised learning
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Artificial intelligence
business
computer
Subjects
Details
- ISSN :
- 18838014 and 13430130
- Volume :
- 21
- Database :
- OpenAIRE
- Journal :
- Journal of Advanced Computational Intelligence and Intelligent Informatics
- Accession number :
- edsair.doi...........613af973c3c71894e0b9862c82ab2c0b