Cellular automata machines: a new environment for modeling
Cellular automata machines: a new environment for modeling
Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
A new kind of science
Theory of Modelling and Simulation
Theory of Modelling and Simulation
CD++: a toolkit to develop DEVS models
Software—Practice & Experience
A survey of evolutionary algorithms for data mining and knowledge discovery
Advances in evolutionary computing
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Data mining with cellular automata
ACM SIGKDD Explorations Newsletter
Discrete-Event Modeling and Simulation: A Practitioner's Approach
Discrete-Event Modeling and Simulation: A Practitioner's Approach
Efficient enhancement on cellular automata for data mining
ICS'09 Proceedings of the 13th WSEAS international conference on Systems
fMRI brain data classification using cellular automata
AIC'10/BEBI'10 Proceedings of the 10th WSEAS international conference on applied informatics and communications, and 3rd WSEAS international conference on Biomedical electronics and biomedical informatics
Data clustering and visualization using cellular automata ants
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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Data mining is the process of extracting patterns from data. A main step in this process is referred to as data classification. In this work, we investigate the use of the Cell-DEVS formalism for classifying data. The cells in a Cell-DEVS based grid are individually very simple but together they can represent complex behavior and are capable of self-organization. Three classifier models are implemented using Cell-DEVS. Different simulation scenarios are presented investigating the effect of Von Neumann versus Moore neighborhood in the classifiers' models. We show that effective classification performance, comparable to those produced by complex data mining techniques, can be obtained from the collective behavior of discrete-event cellular grids.