Using metarules to organize and group discovered association rules

Abdelaziz Berrado, George Runger

Research output: Contribution to journalArticle

43 Citations (Scopus)

Abstract

The high dimensionality of massive data results in the discovery of a large number of association rules. The huge number of rules makes it difficult to interpret and react to all of the rules, especially because many rules are redundant and contained in other rules. We discuss how the sparseness of the data affects the redundancy and containment between the rules and provide a new methodology for organizing and grouping the association rules with the same consequent. It consists of finding metarules, rules that express the associations between the discovered rules themselves. The information provided by the metarules is used to reorganize and group related rules. It is based only on data-determined relationships between the rules. We demonstrate the suggested approach on actual manufacturing data and show its effectiveness on several benchmark data sets.

Original languageEnglish (US)
Pages (from-to)409-431
Number of pages23
JournalData Mining and Knowledge Discovery
Volume14
Issue number3
DOIs
StatePublished - Jun 2007

Fingerprint

Association rules
Association Rules
Redundancy
Grouping
Dimensionality
Express
Manufacturing
Benchmark
Methodology

Keywords

  • Classification
  • Clustering rules
  • Data sparseness
  • Item sets
  • Rules pruning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Information Systems
  • Computer Science (miscellaneous)
  • Mathematics (miscellaneous)

Cite this

Using metarules to organize and group discovered association rules. / Berrado, Abdelaziz; Runger, George.

In: Data Mining and Knowledge Discovery, Vol. 14, No. 3, 06.2007, p. 409-431.

Research output: Contribution to journalArticle

@article{4fde08b6393a4a718203eca3dcebac0c,
title = "Using metarules to organize and group discovered association rules",
abstract = "The high dimensionality of massive data results in the discovery of a large number of association rules. The huge number of rules makes it difficult to interpret and react to all of the rules, especially because many rules are redundant and contained in other rules. We discuss how the sparseness of the data affects the redundancy and containment between the rules and provide a new methodology for organizing and grouping the association rules with the same consequent. It consists of finding metarules, rules that express the associations between the discovered rules themselves. The information provided by the metarules is used to reorganize and group related rules. It is based only on data-determined relationships between the rules. We demonstrate the suggested approach on actual manufacturing data and show its effectiveness on several benchmark data sets.",
keywords = "Classification, Clustering rules, Data sparseness, Item sets, Rules pruning",
author = "Abdelaziz Berrado and George Runger",
year = "2007",
month = "6",
doi = "10.1007/s10618-006-0062-6",
language = "English (US)",
volume = "14",
pages = "409--431",
journal = "Data Mining and Knowledge Discovery",
issn = "1384-5810",
publisher = "Springer Netherlands",
number = "3",

}

TY - JOUR

T1 - Using metarules to organize and group discovered association rules

AU - Berrado, Abdelaziz

AU - Runger, George

PY - 2007/6

Y1 - 2007/6

N2 - The high dimensionality of massive data results in the discovery of a large number of association rules. The huge number of rules makes it difficult to interpret and react to all of the rules, especially because many rules are redundant and contained in other rules. We discuss how the sparseness of the data affects the redundancy and containment between the rules and provide a new methodology for organizing and grouping the association rules with the same consequent. It consists of finding metarules, rules that express the associations between the discovered rules themselves. The information provided by the metarules is used to reorganize and group related rules. It is based only on data-determined relationships between the rules. We demonstrate the suggested approach on actual manufacturing data and show its effectiveness on several benchmark data sets.

AB - The high dimensionality of massive data results in the discovery of a large number of association rules. The huge number of rules makes it difficult to interpret and react to all of the rules, especially because many rules are redundant and contained in other rules. We discuss how the sparseness of the data affects the redundancy and containment between the rules and provide a new methodology for organizing and grouping the association rules with the same consequent. It consists of finding metarules, rules that express the associations between the discovered rules themselves. The information provided by the metarules is used to reorganize and group related rules. It is based only on data-determined relationships between the rules. We demonstrate the suggested approach on actual manufacturing data and show its effectiveness on several benchmark data sets.

KW - Classification

KW - Clustering rules

KW - Data sparseness

KW - Item sets

KW - Rules pruning

UR - http://www.scopus.com/inward/record.url?scp=34147208482&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34147208482&partnerID=8YFLogxK

U2 - 10.1007/s10618-006-0062-6

DO - 10.1007/s10618-006-0062-6

M3 - Article

AN - SCOPUS:34147208482

VL - 14

SP - 409

EP - 431

JO - Data Mining and Knowledge Discovery

JF - Data Mining and Knowledge Discovery

SN - 1384-5810

IS - 3

ER -