Abstract

Son zamanlarda elde edilen bulgular, internetten yapılan alışveriş hacminin tüm dünyada arttığını göstermektedir. İnternetten alışveriş yapan tüketicilerin bölümlendirilmesi de doğal olarak söz konusu işletmelerin etkin bir şekilde çözmesi gereken önemli bir sorundur. Bu çalışmada, internetten alışveriş yapan tüketicilerin internet ile ilişkili yaşam biçimlerine göre bölümlendirilmesi problemi bütünleşik bir veri madenciliği yaklaşımı ile incelenmektedir. Araştırmada kullanılan bütünleşik veri madenciliği yaklaşımı, Kohonen sinir ağı ve birliktelik kuralı madenciliği yöntemlerini içermektedir. Bu iki yöntemin bütünleşik halde kullanımı ile internetten alışveriş yapan tüketicilerin çeşitli pazar bölümlerine ayrılması amaçlanmıştır. Uluslararası eğilimlere benzer olarak, Türkiye?de de alışveriş internetin en önemli kullanım alanlarından biri olmuştur ve araştırma sanayileşmenin oldukça yüksek olduğu bir bölgede yürütülmüştür. Araştırmada kullanılan çok boyutlu analiz için, Clementine 8.1 adlı bir veri madenciliği yazılımı kullanılmıştır.

Keywords

• Bölümlendirme, İnternet Üzerinden Alışveriş, Veri

Segmentation Of Online Shoppers By Means Of An Integrated Data Mining Approach: A Case Study

Abstract

Recent findings indicate that online shopping is increasing significantly all over the world. Segmentation of online shoppers is an important issue of online firms. This paper handles this issue related to internet-related lifestyle descriptors which are effective in determining segments by an integrated data mining approach. The integrated data mining approach which is used in this study consists of self-organizing map (Kohonen) neural network and association rule mining method which are integrated to identify segments of online shoppers. Similar to the international trends, online shopping has become one of the most noticeable yields of internet in Turkey and the research is conducted in a highly industrialized region. For this multi-dimensional analysis, a visual and a robust data mining software Clementine 8.1 is used for the integrated segmentation task in data mining.

Keywords

• Segmentation, Online Shopping, Data Mining, Kohonen, Association Rules

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