Worst-case analysis of rule discovery based on generality and accuracy

Research output: Contribution to journalArticle

Abstract

In this paper, we perform a worst-case analysis of rule discovery based on generality and accuracy. A rule is defined as a probabilistic constraint of true assignment to the class attribute for corresponding examples. In data mining, a rule can be considered as representing an important class of discovered patterns. We accomplish the aforementioned objective by extending a preliminary version of PAC learning, which represents a worst-case analysis for classification. Our analysis consists of two cases: the case in which we try to avoid finding a bad rule, and the case in which we try to avoid overlooking a good rule. Discussions on related work are also provided for PAC learning, multiple comparison, analysis of association rule discovery, and simultaneous reliability evaluation of a discovered rule.

Original languageEnglish
Pages (from-to)630-637
Number of pages8
JournalTransactions of the Japanese Society for Artificial Intelligence
Volume17
Issue number5
DOIs
Publication statusPublished - Dec 1 2002
Externally publishedYes

Fingerprint

Association rules
Data mining

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Worst-case analysis of rule discovery based on generality and accuracy. / Suzuki, Einoshin.

In: Transactions of the Japanese Society for Artificial Intelligence, Vol. 17, No. 5, 01.12.2002, p. 630-637.

Research output: Contribution to journalArticle

@article{97a18bbca0384f52b82d2657d475d1a6,
title = "Worst-case analysis of rule discovery based on generality and accuracy",
abstract = "In this paper, we perform a worst-case analysis of rule discovery based on generality and accuracy. A rule is defined as a probabilistic constraint of true assignment to the class attribute for corresponding examples. In data mining, a rule can be considered as representing an important class of discovered patterns. We accomplish the aforementioned objective by extending a preliminary version of PAC learning, which represents a worst-case analysis for classification. Our analysis consists of two cases: the case in which we try to avoid finding a bad rule, and the case in which we try to avoid overlooking a good rule. Discussions on related work are also provided for PAC learning, multiple comparison, analysis of association rule discovery, and simultaneous reliability evaluation of a discovered rule.",
author = "Einoshin Suzuki",
year = "2002",
month = "12",
day = "1",
doi = "10.1527/tjsai.17.630",
language = "English",
volume = "17",
pages = "630--637",
journal = "Transactions of the Japanese Society for Artificial Intelligence",
issn = "1346-0714",
publisher = "Japanese Society for Artificial Intelligence",
number = "5",

}

TY - JOUR

T1 - Worst-case analysis of rule discovery based on generality and accuracy

AU - Suzuki, Einoshin

PY - 2002/12/1

Y1 - 2002/12/1

N2 - In this paper, we perform a worst-case analysis of rule discovery based on generality and accuracy. A rule is defined as a probabilistic constraint of true assignment to the class attribute for corresponding examples. In data mining, a rule can be considered as representing an important class of discovered patterns. We accomplish the aforementioned objective by extending a preliminary version of PAC learning, which represents a worst-case analysis for classification. Our analysis consists of two cases: the case in which we try to avoid finding a bad rule, and the case in which we try to avoid overlooking a good rule. Discussions on related work are also provided for PAC learning, multiple comparison, analysis of association rule discovery, and simultaneous reliability evaluation of a discovered rule.

AB - In this paper, we perform a worst-case analysis of rule discovery based on generality and accuracy. A rule is defined as a probabilistic constraint of true assignment to the class attribute for corresponding examples. In data mining, a rule can be considered as representing an important class of discovered patterns. We accomplish the aforementioned objective by extending a preliminary version of PAC learning, which represents a worst-case analysis for classification. Our analysis consists of two cases: the case in which we try to avoid finding a bad rule, and the case in which we try to avoid overlooking a good rule. Discussions on related work are also provided for PAC learning, multiple comparison, analysis of association rule discovery, and simultaneous reliability evaluation of a discovered rule.

UR - http://www.scopus.com/inward/record.url?scp=18444403439&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=18444403439&partnerID=8YFLogxK

U2 - 10.1527/tjsai.17.630

DO - 10.1527/tjsai.17.630

M3 - Article

VL - 17

SP - 630

EP - 637

JO - Transactions of the Japanese Society for Artificial Intelligence

JF - Transactions of the Japanese Society for Artificial Intelligence

SN - 1346-0714

IS - 5

ER -