Tuning GSP parameters with GA

Wei Chi Cheng, Ping Yu Hsu, Ming Shien Cheng, Shih Hsiang Huang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In data mining, association rules can be shown when customers buy products, which products will be purchased at the same time. Scholars use this feature to develop market basket analysis to formulate marketing strategies for business. As we know, the data are changing all the time. When new data generate, the old data will be replaced. In the database, time become a very important attribute. And new data mining method have been proposed, called Generalized Sequential Patterns (GSP). GSP uses time stamp to find the product portfolio with sequential patterns. However, the GSP parameter is user-defined. The result of the operation may be unstable, because of the parameter setting incorrectly. Tuning the parameters used in this study combined GSP and Genetic Algorithm (GA) to improve the result continuously, to find the appropriate parameters. In the experiment, we use a medium-sized supermarket verify the results and found that after comparing with random input parameters, the parameters of the proposed method found significantly better than a random set of parameters.

Original languageEnglish
Title of host publicationIntelligence Science and Big Data Engineering
Subtitle of host publicationBig Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers
EditorsZhi-Hua Zhou, Baochuan Fu, Fuyuan Hu, Zhancheng Zhang, Zhi-Yong Liu, Yanning Zhang, Xiaofei He, Xinbo Gao
PublisherSpringer Verlag
Pages159-170
Number of pages12
ISBN (Print)9783319238616
DOIs
StatePublished - 2015
Event5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015 - Suzhou, China
Duration: 14 Jun 201516 Jun 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9243
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015
Country/TerritoryChina
CitySuzhou
Period14/06/1516/06/15

Keywords

  • Generalized sequential patterns
  • Genetic algorithm
  • Sequential pattern mining

Fingerprint

Dive into the research topics of 'Tuning GSP parameters with GA'. Together they form a unique fingerprint.

Cite this