Collective intelligence-based sequential pattern mining approach for marketing data

Kazuaki Tsuboi, Kosuke Shinoda, Hirohiko Suwa, Satoshi Kurihara

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

Abstract

It is important to understand consumer needs correctly and clarify target of goods and service in marketing. In recent years, as information processing technology develops, video image analysis also has become as important tool for customer behavior analysis. It is said that discovering consumers’ purchase patterns of choosing purchased goods may be possible by using video data. Video is sequential temporal data, so time-series data mining technique is necessary. And generally consumer behavior is ambiguous. To respond to these situation, we are now developing a collective intelligence-based sequential pattern mining approach with high robustness and adaptability, and this time, we have succeeded in visualizing the relation of goods that they are continuously touched up by consumer.

Original languageEnglish
Title of host publicationSocial Informatics - SocInfo 2014 International Workshops, Revised Selected Papers
EditorsDaniel McFarland, Luca Maria Aiello
PublisherSpringer Verlag
Pages353-361
Number of pages9
ISBN (Electronic)9783319151670
DOIs
Publication statusPublished - 2015 Jan 1
Externally publishedYes
Event6th International Conference on Social Informatics, SocInfo 2014 - Barcelona, Spain
Duration: 2014 Nov 102014 Nov 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8852
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Conference on Social Informatics, SocInfo 2014
CountrySpain
CityBarcelona
Period14/11/1014/11/13

Keywords

  • Ant colony optimization
  • Marketing
  • Sequential pattern mining

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Tsuboi, K., Shinoda, K., Suwa, H., & Kurihara, S. (2015). Collective intelligence-based sequential pattern mining approach for marketing data. In D. McFarland, & L. M. Aiello (Eds.), Social Informatics - SocInfo 2014 International Workshops, Revised Selected Papers (pp. 353-361). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8852). Springer Verlag. https://doi.org/10.1007/978-3-319-15168-7_44