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Bilgi karmaşıklığı (ICOMP) kriterinin yeni bir sınıfı ile müşteri profili oluşturma ve segmentasyonu uygulaması

A new class of information complexity (ICOMP) criteria with an application to customer profiling and segmentation

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Abstract (2. Language): 
This paper introduces several forms of a new class of information-theoretic measure of complexity criterion called ICOMP as a decision rule for model selection in statistical modeling to help provide new approaches relevant to statistical inference. The practical utility and the importance of ICOMP is illustrated by providing a real numerical example in data mining of mobile phone data for customer profiling and segmentation of mobile phone customers using a novel multi-class support vector machine-recursive feature elimination (MSVM-RFE) method. The approach proposed in this paper outperforms the classical discriminant analysis techniques over 32% in terms of misclassification error rate. This is a remarkable achievement due to using MSVM-RFE hybridized with ICOMP that was not possible using other methods to classify the mobile phone customer data base as a new micro-marketing analytics. This should capture the attention of the mobile phone industry for more refined analysis of their data bases for customer management and retention.
Abstract (Original Language): 
Bu çalısma, ICOMP olarak adlandırılan bilgi karmasıklıgı kriterinin yeni bir sınıfının tanıtımını amaçlamaktadır. Bu kriter, istatistiksel modellemede yeni yaklasımlara yardım saglamaktadır ve en iyi modelin seçilmesinde bir karar kuralı olarak kullanılır. ICOMP’un önemi ve kullanımı, veri madenciliginde yeni bir yöntem olan “çok sınıflı destek vektör makineleri”ni kullanarak (MSVM-RFE), müsteri profili olusturma ve segmantasyonu uygulamasında örnek verilerek gösterilmistir. Bu çalısmada önerilen yeni modelleme, cep telefonu kullanan müsterilerin sınıflandırılmasında, klasik diskriminant analizine göre elde edilen yanlıs sınıflandırma oranının %32’sinden daha iyi bir performans göstermistir. Bu sonuçlar, yeni bir mikro-pazarlama analiz yöntemi olarak kullanılabilir. Ayrıca bu sonuçlar veri tabanlarını daha iyi analizler yaparak sınıflandırmada daha çok müsteri kazanmak isteyen veya ellerindeki müsterileri kaybetmek istemeyen cep telefonu piyasasının dikkatini çekebilir.

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