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
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

Fingerprint

Collective Intelligence
Consumer behavior
Sequential Patterns
Image analysis
Data mining
Marketing
Time series
Mining
Consumer Behaviour
Video Analysis
Ambiguous
Time Series Data
Adaptability
Information Processing
Image Analysis
Data Mining
Customers
Robustness
Target
Necessary

Keywords

  • Ant colony optimization
  • Marketing
  • Sequential pattern mining

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Tsuboi, K., Shinoda, K., Suwa, H., & Kurihara, S. (2015). Collective intelligence-based sequential pattern mining approach for marketing data. In 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

Collective intelligence-based sequential pattern mining approach for marketing data. / Tsuboi, Kazuaki; Shinoda, Kosuke; Suwa, Hirohiko; Kurihara, Satoshi.

Social Informatics - SocInfo 2014 International Workshops, Revised Selected Papers. Springer Verlag, 2015. p. 353-361 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8852).

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

Tsuboi, K, Shinoda, K, Suwa, H & Kurihara, S 2015, Collective intelligence-based sequential pattern mining approach for marketing data. in Social Informatics - SocInfo 2014 International Workshops, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8852, Springer Verlag, pp. 353-361, 6th International Conference on Social Informatics, SocInfo 2014, Barcelona, Spain, 14/11/10. https://doi.org/10.1007/978-3-319-15168-7_44
Tsuboi K, Shinoda K, Suwa H, Kurihara S. Collective intelligence-based sequential pattern mining approach for marketing data. In Social Informatics - SocInfo 2014 International Workshops, Revised Selected Papers. Springer Verlag. 2015. p. 353-361. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-15168-7_44
Tsuboi, Kazuaki ; Shinoda, Kosuke ; Suwa, Hirohiko ; Kurihara, Satoshi. / Collective intelligence-based sequential pattern mining approach for marketing data. Social Informatics - SocInfo 2014 International Workshops, Revised Selected Papers. Springer Verlag, 2015. pp. 353-361 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{82abb5ede02742b7b6343e24f2055130,
title = "Collective intelligence-based sequential pattern mining approach for marketing data",
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.",
keywords = "Ant colony optimization, Marketing, Sequential pattern mining",
author = "Kazuaki Tsuboi and Kosuke Shinoda and Hirohiko Suwa and Satoshi Kurihara",
year = "2015",
month = "1",
day = "1",
doi = "10.1007/978-3-319-15168-7_44",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "353--361",
booktitle = "Social Informatics - SocInfo 2014 International Workshops, Revised Selected Papers",
address = "Germany",

}

TY - GEN

T1 - Collective intelligence-based sequential pattern mining approach for marketing data

AU - Tsuboi, Kazuaki

AU - Shinoda, Kosuke

AU - Suwa, Hirohiko

AU - Kurihara, Satoshi

PY - 2015/1/1

Y1 - 2015/1/1

N2 - 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.

AB - 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.

KW - Ant colony optimization

KW - Marketing

KW - Sequential pattern mining

UR - http://www.scopus.com/inward/record.url?scp=84924389880&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84924389880&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-15168-7_44

DO - 10.1007/978-3-319-15168-7_44

M3 - Conference contribution

AN - SCOPUS:84924389880

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

SP - 353

EP - 361

BT - Social Informatics - SocInfo 2014 International Workshops, Revised Selected Papers

PB - Springer Verlag

ER -