A rough-apriori technique in mining linguistic association rules

Yun Huoy Choo, Azuraliza Abu Bakar, Abdul Razak Hamdan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper has proposed a rough-Apriori based mining technique in mining linguistic association rules focusing on the problem of capturing the numerical interval with linguistic terms in quantitative association rules mining. It uses the rough membership function to capture the linguistic interval before implementing the Apriori algorithm to mine interesting association rules. The performance of conventional quantitative association rules mining algorithm with Boolean reasoning as the discretization method was compared to the proposed technique and the fuzzy-based technique. Five UCI datasets were tested in the 10-fold cross validation experiment settings. The frequent itemsets discovery in the Apriori algorithm was constrained to five iterations comparing to maximum iterations. Results show that the proposed technique has performed comparatively well by generating more specific rules as compared to the other techniques.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages548-555
Number of pages8
Volume5139 LNAI
DOIs
Publication statusPublished - 2008
Event4th International Conference on Advanced Data Mining and Applications, ADMA 2008 - Chengdu
Duration: 8 Oct 200810 Oct 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5139 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other4th International Conference on Advanced Data Mining and Applications, ADMA 2008
CityChengdu
Period8/10/0810/10/08

Fingerprint

Association rules
Association Rules
Linguistics
Rough
Mining
Apriori Algorithm
Association Rule Mining
Iteration
Membership functions
Frequent Itemsets
Interval
Discretization Method
Membership Function
Cross-validation
Fold
Reasoning
Term
Experiments
Experiment

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Choo, Y. H., Abu Bakar, A., & Hamdan, A. R. (2008). A rough-apriori technique in mining linguistic association rules. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5139 LNAI, pp. 548-555). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5139 LNAI). https://doi.org/10.1007/978-3-540-88192-6-55

A rough-apriori technique in mining linguistic association rules. / Choo, Yun Huoy; Abu Bakar, Azuraliza; Hamdan, Abdul Razak.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5139 LNAI 2008. p. 548-555 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5139 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Choo, YH, Abu Bakar, A & Hamdan, AR 2008, A rough-apriori technique in mining linguistic association rules. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 5139 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5139 LNAI, pp. 548-555, 4th International Conference on Advanced Data Mining and Applications, ADMA 2008, Chengdu, 8/10/08. https://doi.org/10.1007/978-3-540-88192-6-55
Choo YH, Abu Bakar A, Hamdan AR. A rough-apriori technique in mining linguistic association rules. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5139 LNAI. 2008. p. 548-555. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-88192-6-55
Choo, Yun Huoy ; Abu Bakar, Azuraliza ; Hamdan, Abdul Razak. / A rough-apriori technique in mining linguistic association rules. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5139 LNAI 2008. pp. 548-555 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{432ac8958d874f09ab637586d2fdc2b1,
title = "A rough-apriori technique in mining linguistic association rules",
abstract = "This paper has proposed a rough-Apriori based mining technique in mining linguistic association rules focusing on the problem of capturing the numerical interval with linguistic terms in quantitative association rules mining. It uses the rough membership function to capture the linguistic interval before implementing the Apriori algorithm to mine interesting association rules. The performance of conventional quantitative association rules mining algorithm with Boolean reasoning as the discretization method was compared to the proposed technique and the fuzzy-based technique. Five UCI datasets were tested in the 10-fold cross validation experiment settings. The frequent itemsets discovery in the Apriori algorithm was constrained to five iterations comparing to maximum iterations. Results show that the proposed technique has performed comparatively well by generating more specific rules as compared to the other techniques.",
author = "Choo, {Yun Huoy} and {Abu Bakar}, Azuraliza and Hamdan, {Abdul Razak}",
year = "2008",
doi = "10.1007/978-3-540-88192-6-55",
language = "English",
isbn = "3540881913",
volume = "5139 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "548--555",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - A rough-apriori technique in mining linguistic association rules

AU - Choo, Yun Huoy

AU - Abu Bakar, Azuraliza

AU - Hamdan, Abdul Razak

PY - 2008

Y1 - 2008

N2 - This paper has proposed a rough-Apriori based mining technique in mining linguistic association rules focusing on the problem of capturing the numerical interval with linguistic terms in quantitative association rules mining. It uses the rough membership function to capture the linguistic interval before implementing the Apriori algorithm to mine interesting association rules. The performance of conventional quantitative association rules mining algorithm with Boolean reasoning as the discretization method was compared to the proposed technique and the fuzzy-based technique. Five UCI datasets were tested in the 10-fold cross validation experiment settings. The frequent itemsets discovery in the Apriori algorithm was constrained to five iterations comparing to maximum iterations. Results show that the proposed technique has performed comparatively well by generating more specific rules as compared to the other techniques.

AB - This paper has proposed a rough-Apriori based mining technique in mining linguistic association rules focusing on the problem of capturing the numerical interval with linguistic terms in quantitative association rules mining. It uses the rough membership function to capture the linguistic interval before implementing the Apriori algorithm to mine interesting association rules. The performance of conventional quantitative association rules mining algorithm with Boolean reasoning as the discretization method was compared to the proposed technique and the fuzzy-based technique. Five UCI datasets were tested in the 10-fold cross validation experiment settings. The frequent itemsets discovery in the Apriori algorithm was constrained to five iterations comparing to maximum iterations. Results show that the proposed technique has performed comparatively well by generating more specific rules as compared to the other techniques.

UR - http://www.scopus.com/inward/record.url?scp=68749085963&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=68749085963&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-88192-6-55

DO - 10.1007/978-3-540-88192-6-55

M3 - Conference contribution

AN - SCOPUS:68749085963

SN - 3540881913

SN - 9783540881919

VL - 5139 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 548

EP - 555

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -