Name
Abstract | ||
|---|---|---|
Cellular Automata are discrete dynamical systems that evol ve following simple and local rules. Despite of its local simplicity, knowledge discovery in CA is a NP problem. This i s the main motivation for using data mining techniques for CA study. The Principal Component Analysis (PCA) is a useful to ol for data mining because it provides a compact and optimal description of data sets. Such feature have been explored tocompute the best subspace which maximizes the projection of the I/O patterns of CA onto the principal axis. The stabilityof the principal components against the input patterns is th e main result of this approach. In this paper we perform such an alysis but in the presence of noise which randomly reverses the CA output values with probabilityp. As expected, the number of principal components increaseswhen the pattern size is increased. However, it seems to remain stable when the pat tern size is unchanged but the noise intensity gets larger. W e describe our experiments and point out further works using K L transform theory and parameter sensitivity analysis. |
Year | Venue | Keywords |
|---|---|---|
2005 | Clinical Orthopaedics and Related Research | cellular automata,sensitivity analysis,data mining,principal component analysis,discrete mathematics,principal component |
Field | DocType | Volume |
Cellular automaton,Discrete mathematics,Combinatorics,Subspace topology,Transform theory,Principal axis theorem,P versus NP problem,Dynamical systems theory,Knowledge extraction,Principal component analysis,Mathematics | Journal | abs/cs/051 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gilson A. Giraldi | 1 | 98 | 21.93 |
| Antonio A. F. Oliveira | 2 | 82 | 15.42 |
| Leonardo Carvalho | 3 | 1 | 2.04 |