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Conference Object Citation - Scopus: 3A methodology for product segmentation using sale transactions(Institute of Electrical and Electronics Engineers Inc., 2018) Peker,S.; Kocyigit,A.; Erhan Eren,P.This paper presents a novel methodology for product segmentation using customers' transactions on products. The proposed methodology introduces FMC model, and utilizes this model's features and clustering algorithms to group products into segments. The applicability of the proposed approach has been demonstrated on data collected by a supermarket chain. The results show that the proposed methodology provides an efficient tool that can be used to identify different product segments and to gain valuable insights about these distinct groups. The resulting product segments can help managers in the inventory management and developing marketing strategies. © 2018 Croatian Society MIPRO.Article Citation - WoS: 13Citation - Scopus: 25A Combined Approach for Customer Profiling in Video on Demand Services Using Clustering and Association Rule Mining(Ieee-inst Electrical Electronics Engineers inc, 2020) Guney, Sinem; Peker, Serhat; Turhan, CigdemThe purpose of this paper is to propose a combined data mining approach for analyzing and profiling customers in video on demand (VoD) services. The proposed approach integrates clustering and association rule mining. For customer segmentation, the LRFMP model is employed alongside the k-means and Apriori algorithms to generate association rules between the identified customer groups and content genres. The applicability of the proposed approach is demonstrated on real-world data obtained from an Internet protocol television (IPTV) operator. In this way, four main customer groups are identified: "high consuming-valuable subscribers", "less consuming subscribers","less consuming-loyal subscribers" and "disloyal subscribers". In detail, for each group of customers, a different marketing strategy or action is proposed, mainly campaigns, special-day promotions, discounted materials, offering favorite content, etc. Further, genres preferred by these customer segments are extracted using the Apriori algorithm. The results obtained from this case study also show that the proposed approach provides an efficient tool to form different customer segments with specific content rental characteristics, and to generate useful association rules for these distinct groups. The proposed combined approach in this research would be beneficial for IPTV service providers to implement effective CRM and customer-based marketing strategies.Conference Object A Methodology for Product Segmentation Using Sale Transactions(Ieee, 2018) Peker, Serhat; Kocyigit, Altan; Eren, P. ErhanThis paper presents a novel methodology for product segmentation using customers' transactions on products. The proposed methodology introduces FMC model, and utilizes this model's features and clustering algorithms to group products into segments. The applicability of the proposed approach has been demonstrated on data collected by a supermarket chain. The results show that the pro-posed methodology provides an efficient tool that can be used to identify different product segments and to gain valuable insights about these distinct groups. The resulting product segments can help managers in the inventory management and developing marketing strategies.Conference Object Citation - Scopus: 2Image Tag Refinement With Self Organizing Maps(Institute of Electrical and Electronics Engineers Inc., 2019) Ustunkok,T.; Acar,O.C.; Karakaya,M.Nowadays, data sharing has become faster than ever. This speed demands novel search methods. Most popular way of accessing the data is to search its tag. Therefore, creating tags, captions from an image is a research area that gains reputation rapidly. In this study, we aim to refine image captions by utilizing Self Organizing Maps. We extract image and caption pairs as feature vectors and then cluster those vectors. Vectors with similar content clustered close to each other. With the help of those clusters, we hope to get some relevant tags that do not exist in the original tags. We performed extensive experiments and presented our initial results. According to these results, the proposed model performs reasonably well with a 54% precision score. Finally, we conclude our work by providing a list of future work. © 2019 IEEE.
