• Journal of Korean Institute of Industrial Engineers
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• v.27 no.4
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• pp.394-405
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• 2001

Since most real-world application data involve continuous-valued attributes, properly addressing the discretization process for constructing a decision tree is an important problem. A continuous-valued attribute is typically discretized during decision tree generation by partitioning its range into two intervals recursively. In this paper, by removing the restriction to the binary discretization, we present a hybrid multi-interval discretization algorithm for discretizing the range of continuous-valued attribute into multiple intervals. On the basis of experiment using semiconductor etching machine, it has been verified that our discretization algorithm constructs a more efficient incremental decision tree compared to previously proposed discretization algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.327-332
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• 2003

A new discretization method for nonlinear system with time-delay is proposed. It is based on the well-known Taylor series expansion and the zero-order hold (ZOH) assumption. We know that a discretization of linear system can be obtained with the ZOH assumption and within the sampling interval. A similar line of thinking is available in nonlinear case. The mathematical structure of the new discretization method is explored and under the structure, the sampled-data representation of nonlinear system including time-delay is computed. Provided that the discrete form of the single input nonlinear system with time-delay is derived, this result is easily extended to nonlinear system with multi-input time-delay. For simplicity two inputs are considered in this study. It is enough to generalize that of multiple inputs. Finally, the time-discretization of non-affine nonlinear system with time-delay is investigated for apply all nonlinear system

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.60-64
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• 2010

To discover association rules from nontransactional data, there have been many studies on discretization of attribute values. These studies do not reflect the change of discovered rules' confidence according to the change of the ranges of the discretized attributes, and perform the discretization stage and the rule discovery stage independently. This causes the ranges of attributes not properly discretized, thereby making the rules having high confidence excluded in the result set. To solve this problem, we propose a novel method that performs the discretization and rule discovery stages simultaneously in order to discretize ranges of attributes in such a way that the rules having high confidence are discovered well. To the end, we perform hierarchical clustering on the attributes in the right hand side of rules, then do characterization on every cluster thus obtained. The experimental result demonstrates that our method discovers the rules having high confidence better than existing methods.