Abstract
In this paper, using individual tobacco panel data, a novel and behavioral approach based on sequence clustering techniques is proposed to have a deeper understanding of different behavior types of Turkish tobacco users during the consecutive price markups of 6th April and 2nd May in 2019. To achieve this, main brands before markups are determined for each of the 5052 individuals. Then, having some prior assumptions, their purchase behaviors are obtained as time-stamped event sequences. Finally, while a portion of the obtained sequences which are less complex are segmented with empirical analyses, the rest of them are segmented using hierarchical clustering with optimal matching event (OME) distance. Results suggested seven main type of behavior among the tobacco users in Turkey during the markup period.
Original language | English |
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Title of host publication | Intelligent and Fuzzy Techniques |
Subtitle of host publication | Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference |
Editors | Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A. Cagri Tolga |
Publisher | Springer |
Pages | 79-86 |
Number of pages | 8 |
ISBN (Print) | 9783030511555 |
DOIs | |
Publication status | Published - 2021 |
Event | International Conference on Intelligent and Fuzzy Systems, INFUS 2020 - Istanbul, Turkey Duration: 21 Jul 2020 → 23 Jul 2020 |
Publication series
Name | Advances in Intelligent Systems and Computing |
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Volume | 1197 AISC |
ISSN (Print) | 2194-5357 |
ISSN (Electronic) | 2194-5365 |
Conference
Conference | International Conference on Intelligent and Fuzzy Systems, INFUS 2020 |
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Country/Territory | Turkey |
City | Istanbul |
Period | 21/07/20 → 23/07/20 |
Bibliographical note
Publisher Copyright:© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keywords
- Hierarchical clustering
- Individual tobacco panel
- Sequence analysis
- Sequence clustering
- Unsupervised learning