Yazar "Öztayşi, Başar" seçeneğine göre listele

Listeleniyor 1 - 7 / 7
  • [ X ]
    Öğe
    From indoor paths to gender prediction with soft clustering
    (IOS Press, 2020) Doğan, Onur; Öztayşi, Başar
    Customer-based practices enable benefits to organizations in a contentious business. Offering individualized proposals increase customer loyalty to be able to afloat. Understanding customers is a vital difficulty to perform personalized recommendations. As a demographic feature, gender information essentially cannot be captured by human tracking technologies. Hence, several procedures are improved to predict undiscovered gender information. In the research, the followed indoor paths in a shopping mall are used to predict customer genders using fuzzy c-medoids, one of the soft clustering techniques. A Levenshtein-based fuzzy classification methodology is proposed the followed paths as string data. Although some studies focused on gender prediction, no research has centered on path-oriented. The novelty of the investigation is to analyze customer path data for the gender classes.
  • Küçük Resim
    Öğe
    Fuzzy association rule mining approach to identify e-commerce product association considering sales amount
    (Springer Heidelberg, 2022) Doğan, Onur; Kem, Furkan Can; Öztayşi, Başar
    Online stores assist customers in buying the desired products online. Great competition in the e-commerce sector necessitates technology development. Many e-commerce systems not only present products but also offer similar products to increase online customer interest. Due to high product variety, analyzing products sold together similar to a recommendation system is a must. This study methodologically improves the traditional association rule mining (ARM) method by adding fuzzy set theory. Besides, it extends the ARM by considering not only items sold but also sales amounts. Fuzzy association rule mining (FARM) with the Apriori algorithm can catch the customers' choice from historical transaction data. It discovers fuzzy association rules from an e-commerce company to display similar products to customers according to their needs in amount. The experimental result shows that the proposed FARM approach produces much information about e-commerce sales for decision-makers. Furthermore, the FARM method eliminates some traditional rules considering their sales amount and can produce some rules different from ARM.
  • [ X ]
    Öğe
    Gender prediction from classified indoor customer paths by fuzzy c-medoids clustering
    (Springer Verlag, 2020) Doğan, Onur; Öztayşi, Başar
    Customer oriented systems provides advantages to companies in competitive environment. Understanding customers is a fundamental problem to present individualized offers. Gender information, which is one of the demographic information of customers, mainly cannot be obtained by data collection technologies. Therefore, various techniques are developed to predict unknown genders of customers. In this study, customer genders are predicted from their paths in a shopping mall using fuzzy set theory. A fuzzy classification method based on Levenshtein distance is developed for string data that refer to the indoor customer paths. Although there are several ways to predict the gender, no study has focused on path-based gender classification. The originality of the study is to classify customer data into the gender classes using indoor paths. © 2020, Springer Nature Switzerland AG.
  • Küçük Resim
    Öğe
    Genders prediction from indoor customer paths by Levenshtein-based fuzzy kNN
    (Pergamon-Elsevier Science Ltd, 2019) Doğan, Onur; Öztayşi, Başar
    Companies have an advantage over the competitors if they can present customized offers to customers. Demographic information of customers is critical for the companies to develop individualized systems. While current technologies make it easy to collect customer data, the main problem is that demographic data are usually incomplete. Hence, several methods are developed to predict unknown genders of customers. In this study, customer genders are predicted from their paths in a shopping mall using fuzzy sets. A fuzzy classification method based on Levenshtein distance is developed for string data that refer to the indoor customer paths. Although there are several ways to predict the gender, no study has focused on path-based gender classification. The originality of the research is to classify customer data into the gender classes using indoor paths. (C) 2019 Elsevier Ltd. All rights reserved.
  • [ X ]
    Öğe
    Process mining application for analysis of customer’s different visits in a shopping mall
    (Springer Verlag, 2020) Doğan, Onur; Fernandez-Llatas, Carlos; Öztayşi, Başar
    Indoor customers may have different purposes to visit a shopping mall. Understanding the visiting aims results in better customer relationship management. One of the ways to explain the customer purpose is to discover customer paths. Customers mainly visit stores related to their purposes. The main problem is to discover customer paths from paths. Since customers have changeable mood and there are many stores in a shopping mall, customer paths are generally too complex to evaluate. To overcome that problem, we use process mining technique. Process mining is a technique that has some algorithms to discover business processes from event logs in the databases. In this study, we consider the visited stores as an activity in a process. PALIA Suite, a process mining tool that includes several clustering methods for processes, is used to discover and cluster indoor customer paths to evaluate differences among the visits. © 2020, Springer Nature Switzerland AG.
  • [ X ]
    Öğe
    Process-centric customer analytics: Understanding visit purposes of predicted age groups with discovered paths
    (Old City Publishing Inc, 2020) Doğan, Onur; Öztayşi, Başar; Fernandez-Llatas, Carlos
    In a general manner, customer relationship management engages in understanding customer needs and meet them. Most of the investments are either far from customer needs or based on a primitive data collection method. However, customers mainly do not behave with the same ideas to shop in the retail domain. Several studies aim to understand the visiting purposes of customers using various methods. This study seeks to uncover the visit purposes of customers from their paths. Due to customers' unpredictable moods and plenty of stores in the shopping mall, the discovered paths are usually too complicated to analyze. Process mining that can overcome this obstacle is a method that creates process flows from event logs in the databases. In this study, the visited stores were seen as an activity in a business process. PALIA, a discovery algorithm in process mining, was applied to find and cluster customer paths. This study contributes to the literature by examining customer needs from their indoor paths, which were created by the PALIA algorithm. It facilitates to analyze discrepancies among the visits for the same customer. Moreover, the discovered paths are considered according to the age groups predicted by Levenshtein fuzzy kNN (L-FkNN).
  • [ X ]
    Öğe
    Segmentation of indoor customer paths using intuitionistic fuzzy clustering: Process mining visualization
    (IOS Press, 2020) Doğan, Onur; Öztayşi, Başar; Fernandez-Llatas, Carlos
    There are some studies and methods in the literature to understand customer needs and behaviors from the path. However, path analysis has a complex structure because the many customers can follow many different paths. Therefore, clustering methods facilitate the analysis of the customer location data to evaluate customer behaviors. Therefore, we aim to understand customer behavior by clustering their paths. We use an intuitionistic fuzzy c-means clustering (IFCM) algorithm for two-dimensional indoor customer data; case durations and the number of visited locations. Customer location data was collected by Bluetooth-based technology devices from one of the major shopping malls in Istanbul. Firstly, we create customer paths from customer location data by using process mining that is a technique that can be used to increase the understandability of the IFCM results. Moreover, we show with this study that fuzzy methods and process mining technique can be used together to analyze customer paths and gives more understandable results. We also present behavioral changes of some customers who have a different visit by inspecting their clustered paths.