The optimal decision rules discovery

Huaicheng Kou; Yunjie Wu; Lijun Zhao

doi:10.1109/FITME.2010.5656313

The optimal decision rules discovery

Huaicheng Kou^*
, Yunjie Wu
, Lijun Zhao

^*Corresponding author for this work

School of Automation Science and Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Aiming at the data set expressed by decision-list, proposed an algorithm of optimum decision rules discovery based on granule computing. It increased the efficiency of decision rules mining by two ways, improving speed of getting frequent item sets with granule computing and deleting invalid candidate sets. Finally the result of calculating test showed the algorithm is validity.

Original language	English
Title of host publication	2010 International Conference on Future Information Technology and Management Engineering, FITME 2010
Pages	349-352
Number of pages	4
DOIs	https://doi.org/10.1109/FITME.2010.5656313
State	Published - 2010
Event	2010 International Conference on Future Information Technology and Management Engineering, FITME 2010 - Changzhou, China Duration: 9 Oct 2010 → 10 Oct 2010

Publication series

Name	2010 International Conference on Future Information Technology and Management Engineering, FITME 2010
Volume	1

Conference

Conference	2010 International Conference on Future Information Technology and Management Engineering, FITME 2010
Country/Territory	China
City	Changzhou
Period	9/10/10 → 10/10/10

Keywords

Association rule
Decision table
Granule computing
Optimal decision rule

Access to Document

10.1109/FITME.2010.5656313

Cite this

@inproceedings{162ea2df918c4dfbbbcef968e1f82dac,

title = "The optimal decision rules discovery",

abstract = "Aiming at the data set expressed by decision-list, proposed an algorithm of optimum decision rules discovery based on granule computing. It increased the efficiency of decision rules mining by two ways, improving speed of getting frequent item sets with granule computing and deleting invalid candidate sets. Finally the result of calculating test showed the algorithm is validity.",

keywords = "Association rule, Decision table, Granule computing, Optimal decision rule",

author = "Huaicheng Kou and Yunjie Wu and Lijun Zhao",

year = "2010",

doi = "10.1109/FITME.2010.5656313",

language = "英语",

isbn = "9781424490882",

series = "2010 International Conference on Future Information Technology and Management Engineering, FITME 2010",

pages = "349--352",

booktitle = "2010 International Conference on Future Information Technology and Management Engineering, FITME 2010",

note = "2010 International Conference on Future Information Technology and Management Engineering, FITME 2010 ; Conference date: 09-10-2010 Through 10-10-2010",

}

Kou, H, Wu, Y & Zhao, L 2010, The optimal decision rules discovery. in 2010 International Conference on Future Information Technology and Management Engineering, FITME 2010., 5656313, 2010 International Conference on Future Information Technology and Management Engineering, FITME 2010, vol. 1, pp. 349-352, 2010 International Conference on Future Information Technology and Management Engineering, FITME 2010, Changzhou, China, 9/10/10. https://doi.org/10.1109/FITME.2010.5656313

The optimal decision rules discovery. / Kou, Huaicheng; Wu, Yunjie; Zhao, Lijun.
2010 International Conference on Future Information Technology and Management Engineering, FITME 2010. 2010. p. 349-352 5656313 (2010 International Conference on Future Information Technology and Management Engineering, FITME 2010; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - The optimal decision rules discovery

AU - Kou, Huaicheng

AU - Wu, Yunjie

AU - Zhao, Lijun

PY - 2010

Y1 - 2010

N2 - Aiming at the data set expressed by decision-list, proposed an algorithm of optimum decision rules discovery based on granule computing. It increased the efficiency of decision rules mining by two ways, improving speed of getting frequent item sets with granule computing and deleting invalid candidate sets. Finally the result of calculating test showed the algorithm is validity.

AB - Aiming at the data set expressed by decision-list, proposed an algorithm of optimum decision rules discovery based on granule computing. It increased the efficiency of decision rules mining by two ways, improving speed of getting frequent item sets with granule computing and deleting invalid candidate sets. Finally the result of calculating test showed the algorithm is validity.

KW - Association rule

KW - Decision table

KW - Granule computing

KW - Optimal decision rule

UR - https://www.scopus.com/pages/publications/78651455581

U2 - 10.1109/FITME.2010.5656313

DO - 10.1109/FITME.2010.5656313

M3 - 会议稿件

AN - SCOPUS:78651455581

SN - 9781424490882

T3 - 2010 International Conference on Future Information Technology and Management Engineering, FITME 2010

SP - 349

EP - 352

BT - 2010 International Conference on Future Information Technology and Management Engineering, FITME 2010

T2 - 2010 International Conference on Future Information Technology and Management Engineering, FITME 2010

Y2 - 9 October 2010 through 10 October 2010

ER -

The optimal decision rules discovery

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this