Techniques of acceleration for association rule induction with pseudo-artificial life algorithm

Masaaki Kanakubo, Masafumi Hagiwara

Research output: Contribution to journalArticle

Abstract

Frequent pattern mining is an important problem in data mining. Generally, the number of potential rules grows rapidly as the size of the database increases. It is therefore hard for a user to extract association rules. To avoid such a difficulty, we propose a new method for association rule induction using a pseudo-artificial life approach. The proposed method is to decide whether there exists an item set which contains N or more items in two transactions. If it exists, a series of item sets that are contained in the part of the transactions will be recorded. The iteration of this step contributes to the extraction of association rules. It is not necessary to calculate a huge number of candidate rules. In an evaluation test, the authors compared the extracted association rules using our method with rules obtained with other algorithms such as the a priori algorithm. In an evaluation using a huge retail market basket data set, our method was found to be approximately 10 to 20 times faster than the a priori algorithm and its variants.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalElectronics and Communications in Japan
Volume93
Issue number2
DOIs
Publication statusPublished - 2010 Feb

Fingerprint

Rule Induction
Artificial Life
Association rules
Association Rules
induction
Transactions
Frequent Pattern Mining
Evaluation
Data mining
Data Mining
baskets
data mining
evaluation
Iteration
Calculate
Necessary
Series
iteration

Keywords

  • Artificial life
  • Association rules
  • Data mining

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Physics and Astronomy(all)
  • Signal Processing
  • Applied Mathematics

Cite this

Techniques of acceleration for association rule induction with pseudo-artificial life algorithm. / Kanakubo, Masaaki; Hagiwara, Masafumi.

In: Electronics and Communications in Japan, Vol. 93, No. 2, 02.2010, p. 1-11.

Research output: Contribution to journalArticle

@article{f0c44ee9432844158cbb3f74a196a4ae,
title = "Techniques of acceleration for association rule induction with pseudo-artificial life algorithm",
abstract = "Frequent pattern mining is an important problem in data mining. Generally, the number of potential rules grows rapidly as the size of the database increases. It is therefore hard for a user to extract association rules. To avoid such a difficulty, we propose a new method for association rule induction using a pseudo-artificial life approach. The proposed method is to decide whether there exists an item set which contains N or more items in two transactions. If it exists, a series of item sets that are contained in the part of the transactions will be recorded. The iteration of this step contributes to the extraction of association rules. It is not necessary to calculate a huge number of candidate rules. In an evaluation test, the authors compared the extracted association rules using our method with rules obtained with other algorithms such as the a priori algorithm. In an evaluation using a huge retail market basket data set, our method was found to be approximately 10 to 20 times faster than the a priori algorithm and its variants.",
keywords = "Artificial life, Association rules, Data mining",
author = "Masaaki Kanakubo and Masafumi Hagiwara",
year = "2010",
month = "2",
doi = "10.1002/ecj.10225",
language = "English",
volume = "93",
pages = "1--11",
journal = "Electronics and Communications in Japan",
issn = "1942-9533",
publisher = "Scripta Technica",
number = "2",

}

TY - JOUR

T1 - Techniques of acceleration for association rule induction with pseudo-artificial life algorithm

AU - Kanakubo, Masaaki

AU - Hagiwara, Masafumi

PY - 2010/2

Y1 - 2010/2

N2 - Frequent pattern mining is an important problem in data mining. Generally, the number of potential rules grows rapidly as the size of the database increases. It is therefore hard for a user to extract association rules. To avoid such a difficulty, we propose a new method for association rule induction using a pseudo-artificial life approach. The proposed method is to decide whether there exists an item set which contains N or more items in two transactions. If it exists, a series of item sets that are contained in the part of the transactions will be recorded. The iteration of this step contributes to the extraction of association rules. It is not necessary to calculate a huge number of candidate rules. In an evaluation test, the authors compared the extracted association rules using our method with rules obtained with other algorithms such as the a priori algorithm. In an evaluation using a huge retail market basket data set, our method was found to be approximately 10 to 20 times faster than the a priori algorithm and its variants.

AB - Frequent pattern mining is an important problem in data mining. Generally, the number of potential rules grows rapidly as the size of the database increases. It is therefore hard for a user to extract association rules. To avoid such a difficulty, we propose a new method for association rule induction using a pseudo-artificial life approach. The proposed method is to decide whether there exists an item set which contains N or more items in two transactions. If it exists, a series of item sets that are contained in the part of the transactions will be recorded. The iteration of this step contributes to the extraction of association rules. It is not necessary to calculate a huge number of candidate rules. In an evaluation test, the authors compared the extracted association rules using our method with rules obtained with other algorithms such as the a priori algorithm. In an evaluation using a huge retail market basket data set, our method was found to be approximately 10 to 20 times faster than the a priori algorithm and its variants.

KW - Artificial life

KW - Association rules

KW - Data mining

UR - http://www.scopus.com/inward/record.url?scp=73949146548&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=73949146548&partnerID=8YFLogxK

U2 - 10.1002/ecj.10225

DO - 10.1002/ecj.10225

M3 - Article

AN - SCOPUS:73949146548

VL - 93

SP - 1

EP - 11

JO - Electronics and Communications in Japan

JF - Electronics and Communications in Japan

SN - 1942-9533

IS - 2

ER -