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

Year 2010, Volume: 39 Issue: 2, 370 - 398, 02.12.2009

Hamparsun Bozdogan

Abstract

Bu çalışma, ICOMP olarak adlandırılan bilgi karmaşıklığı kriterinin yeni bir sınıfının tanıtımını amaçlamaktadır. Bu kriter, istatistiksel modellemede yeni yaklaşımlara yardım sağlamaktadır ve en iyi modelin seçilmesinde bir karar kuralı olarak kullanılır. ICOMP’un önemi ve kullanımı, veri madenciliğinde yeni bir yöntem olan “çok sınıflı destek vektör makineleri”ni kullanarak (MSVMRFE), müşteri profili oluşturma ve segmantasyonu uygulamasında örnek verilerek gösterilmiştir. Bu çalışmada önerilen yeni modelleme, cep telefonu kullanan müşterilerin sınıflandırılmasında, klasik diskriminant analizine göre elde edilen yanlış sınıflandırma oranının %32’sinden daha iyi bir performans göstermiştir. Bu sonuçlar, yeni bir mikro-pazarlama analiz yöntemi olarak kullanılabilir. Ayrıca bu sonuçlar veri tabanlarını daha iyi analizler yaparak sınıflandırmada daha çok müşteri kazanmak isteyen veya ellerindeki müşterileri kaybetmek istemeyen cep telefonu piyasasının dikkatini çekebilir. 

Keywords

Yeni ICOMP sınıfı kriterler, kovaryans karmaşıklığı, tahminlenmiş Fisher bilgi matrisi (FIM) tersi, model seçme, çok sınıflı destek vektör makineleri – yinelemeli özellikli eleme, müşteri profili ve segmantasyonu

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

Abstract

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. 

Keywords

ICOMP class of criteria, covariance complexity, estimated inverse-Fisher information matrix (FIM), model selection, multi-class support vector machine-recursive feature elimination (MSVM-RFE), customer profiling and segmentation

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