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Has files: falseSubject: data mining

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    Article
    Citation - WoS: 13
    Citation - Scopus: 24
    A 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, Cigdem
    The 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.
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    A Prediction Study About the Pandemic Era Based on Machine Learning Methods
    (Auricle Global Society of Education and Research, 2021) Eryılmaz,M.; Eryılmaz, Meltem; Yalçınkaya,F.; Kara, Erdi; Ertan,Ö.; Kara,E.; Eryılmaz, Meltem; Kara, Erdi; Mathematics; Computer Engineering; Mathematics; Computer Engineering
    Coronavirus pandemic has been going on since late 2019, millions of people died worldwide, vaccination has recently started in many countries and new strategies are sought by countries since they are still struggling to defeat the virus. So, this research is made to predict the possible ending time of the coronavirus pandemic in Turkey using data mining and statistical studies. Data mining is a computer science study that processes large amounts of data and produces new useful information. It is especially used to support decision making in companies today. So, this project could support the decision making of authorities in developing an effective strategy against the on-going pandemic. During the research we have practiced on Turkey’s coronavirus and vaccination data between 13 January 2021 and 28 May 2021. We used Rapidminer and the Random Forest method for data mining. After all the simulations we have applied and observed during our research, it was clearly seen that vaccination parameters were decreasing the new cases. Also, the stringency index did not affect the new cases. As a conclusion of our research and observations, we think that the government should vaccinate as many people as it can in order to relax restrictions for the last time. © 2021 Authors. All rights reserved.