Cellular automata machines: a new environment for modeling
Cellular automata machines: a new environment for modeling
Instance-Based Learning Algorithms
Machine Learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
A brief history of cellular automata
ACM Computing Surveys (CSUR)
A new kind of science
Proceedings of the Second Conference on Cellular Automata for Research and Industry
ACRI '96 Proceedings of the Second Conference on Cellular Automata for Research and Industry
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Efficient enhancement on cellular automata for data mining
ICS'09 Proceedings of the 13th WSEAS international conference on Systems
Discovery by genetic algorithm of cellular automata rules for pattern reconstruction task
ACRI'10 Proceedings of the 9th international conference on Cellular automata for research and industry
Learning cellular automata rules for pattern reconstruction task
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
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
Detecting grain boundaries in deformed rocks using a cellular automata approach
Computers & Geosciences
Data mining with cellular discrete event modeling and simulation
Proceedings of the 45th Annual Simulation Symposium
Learning cellular automata rules for binary classification problem
The Journal of Supercomputing
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A cellular automaton is a discrete, dynamical system composed of very simple, uniformly interconnected cells. Cellular automata may be seen as an extreme form of simple, localized, distributed machines. Many researchers are familiar with cellular automata through Conway's Game of Life. Researchers have long been interested in the theoretical aspects of cellular automata. This article explores the use of cellular automata for data mining, specifically for classification tasks. We demonstrate that reasonable generalization behavior can be achieved as an emergent property of these simple automata.