### Abstract

Knowledge acquisition continuous to be a challenging and time consuming task in building decision support systems. Among the dominant methods, rule induction algorithms such as ID3 and C4.5 are widely used to extract rules from examples. The thrust of these algorithms is how they discriminate the given attributes based on information measure for building and determining the nodes in decision tree. In particular, the main focus of these algorithms is on how to select the most appropriate attribute at each level of the decision tree process. This paper proposes an algorithm for rule induction for continuous data. The proposed algorithm uses an analysis of variance criterion for information measure in discriminating the given attributes for building the decision tree for continuous of data.

Original language | English (US) |
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Pages (from-to) | 489-494 |

Number of pages | 6 |

Journal | Conference Proceedings - IEEE SOUTHEASTCON |

State | Published - Nov 9 2005 |

Event | IEEE Southeastcon 2005: Excellence in Engineering, Science and Technology - Ft. Lauderdale, United Kingdom Duration: Apr 8 2005 → Apr 10 2005 |

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### ASJC Scopus subject areas

- Electrical and Electronic Engineering

### Cite this

**A rule induction algorithm for continuous data using analysis of variance.** / Konda, Ramesh; Rajurkar, K. P.

Research output: Contribution to journal › Conference article

*Conference Proceedings - IEEE SOUTHEASTCON*, pp. 489-494.

}

TY - JOUR

T1 - A rule induction algorithm for continuous data using analysis of variance

AU - Konda, Ramesh

AU - Rajurkar, K. P.

PY - 2005/11/9

Y1 - 2005/11/9

N2 - Knowledge acquisition continuous to be a challenging and time consuming task in building decision support systems. Among the dominant methods, rule induction algorithms such as ID3 and C4.5 are widely used to extract rules from examples. The thrust of these algorithms is how they discriminate the given attributes based on information measure for building and determining the nodes in decision tree. In particular, the main focus of these algorithms is on how to select the most appropriate attribute at each level of the decision tree process. This paper proposes an algorithm for rule induction for continuous data. The proposed algorithm uses an analysis of variance criterion for information measure in discriminating the given attributes for building the decision tree for continuous of data.

AB - Knowledge acquisition continuous to be a challenging and time consuming task in building decision support systems. Among the dominant methods, rule induction algorithms such as ID3 and C4.5 are widely used to extract rules from examples. The thrust of these algorithms is how they discriminate the given attributes based on information measure for building and determining the nodes in decision tree. In particular, the main focus of these algorithms is on how to select the most appropriate attribute at each level of the decision tree process. This paper proposes an algorithm for rule induction for continuous data. The proposed algorithm uses an analysis of variance criterion for information measure in discriminating the given attributes for building the decision tree for continuous of data.

UR - http://www.scopus.com/inward/record.url?scp=27544431569&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=27544431569&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:27544431569

SP - 489

EP - 494

JO - Conference Proceedings - IEEE SOUTHEASTCON

JF - Conference Proceedings - IEEE SOUTHEASTCON

SN - 0734-7502

ER -