Özet
Association rule mining (ARM) refers to a procedure that focuses on finding frequent patterns in various data sources. The most commonly used area is the retail sales data and rules which show an association between sales of two products are investigated. To this end, sales data is preprocessed, and algorithms are used to find the association rules by using the specific threshold values of Support and Confidence parameters. In this study, we investigated the effects of using fuzzy clustering with association rule mining. A case study from the E-commerce area is selected and sales data for a specific period is analyzed. First ARM is applied to the whole data, then the data is segmented by using Fuzzy Clustering, and ARM is applied to all segments. Later the resulting rules are compared and the effects of segmentation on ARM results are analyzed.
Orijinal dil | İngilizce |
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Ana bilgisayar yayını başlığı | Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference |
Editörler | Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, A. Cagri Tolga, Selcuk Cebi |
Yayınlayan | Springer Science and Business Media Deutschland GmbH |
Sayfalar | 112-118 |
Sayfa sayısı | 7 |
ISBN (Basılı) | 9783031091728 |
DOI'lar | |
Yayın durumu | Yayınlandı - 2022 |
Etkinlik | International Conference on Intelligent and Fuzzy Systems, INFUS 2022 - Izmir, Turkey Süre: 19 Tem 2022 → 21 Tem 2022 |
Yayın serisi
Adı | Lecture Notes in Networks and Systems |
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Hacim | 504 LNNS |
ISSN (Basılı) | 2367-3370 |
ISSN (Elektronik) | 2367-3389 |
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???event.eventtypes.event.conference??? | International Conference on Intelligent and Fuzzy Systems, INFUS 2022 |
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Ülke/Bölge | Turkey |
Şehir | Izmir |
Periyot | 19/07/22 → 21/07/22 |
Bibliyografik not
Publisher Copyright:© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.