KARMA TİPTEKİ VERİLERİ KAMILA, K-ORTALAMALAR, KORTAYLAR ve K-PROTOTİPLER ALGORİTMALARIYLA KÜMELEME: KARŞILAŞTIRMALI BİR UYGULAMA

Emrah Bilgiç

doi:10.37880/cumuiibf.507182

EN TR

A Comparative Application on Clustering of Mixed-type Data Sets with kamila, k-means, k-medoids and k-prototypes Algorithms

Abstract

Cluster Analysis is one of the crucial tools which is being used in many areas of scientific researches. As known, there are many algorithms for performing Cluster Analysis.
Nowadays, the main two debates relating to these algorithms are; which one to use for mixedtype data sets and how to decide selecting the best number of clusters. In this study, KAMILA algorithm which is created very ambitiously and other algorithms used before KAMILA such as k-means, k-medoids and k-prototypes algorithms will be performed for clustering the values
of different scaled variables. With this aim, a data set of a grocery store in Istanbul will be analyzed. The company has stores in different districts of Istanbul and the customers have different demographic characteristics and different purchasing behaviors. The data set provided for 999 customers includes information such as; whether the customers are purchasing the product categories that are crucial for the company's profitability and how much the total price of the purchased items are. These data were subjected to clustering analysis for customer segmentation. As a result, it is observed that KAMILA algorithm can successfully identify the customers in the segment that can be named the gold segment.

Keywords

KARMA TİPTEKİ VERİLERİ KAMILA, K-ORTALAMALAR, KORTAYLAR ve K-PROTOTİPLER ALGORİTMALARIYLA KÜMELEME: KARŞILAŞTIRMALI BİR UYGULAMA

Öz

Kümeleme Analizi Sosyal Bilimlerden Fen Bilimlerine birçok alanda yaygın olarak kullanılan önemli bir araçtır. Kümeleme Analizini gerçekleştirebilmek için hazırlanmış pek çok algoritma mevcuttur. Günümüzde bu algoritmalar ile ilgili olarak en çok tartışılan hususlardan ilk ikisinin, karma tipteki veri setleri için hangi kümeleme algoritmasının kullanılması gerektiği ve en iyi küme sayısının nasıl belirlenebileceği olduğu söylenebilir. Bu çalışmada, farklı ölçeklerle ölçülmüş karma tipteki değişkenlerin değerlerini içeren bir veri seti, bu tip veriler için yeni ve çok iddialı bir şekilde oluşturulmuş olan KAMILA algoritması ile analiz edilecektir. Daha sonra veri seti bu algoritmadan önce karma tipteki veriler için kullanılagelen k-ortalamalar, k-ortaylar ve k-prototipler gibi algoritmalarla da kümelere ayrılacaktır. Bu doğrultuda, İstanbul’da faaliyet gösteren yerel bir süpermarket zincirinden sağlanan alışveriş işlem verileri, R programlama dili kullanılarak analiz edilmiştir. Mağazaları İstanbul’un farklı semtlerinde bulunan bu firmanın müşterileri farklı demografik özelliklere ve farklı satın alma davranışlarına sahiptir. İşlem kolaylığı açısından 999 müşteri için sağlanmış olan veri kümesi, müşterilerin firmanın kârlılığı açısından önem arz eden ürün kategorilerinden alış veriş yapıp yapmadıklarını ve satın alınan ürünlerin toplam fiyatının ne kadar olduklarını içermektedir. Bu veriler müşteri segmentasyonu amacıyla kümeleme analizine tâbi tutulmuştur. Sonuç olarak, KAMILA algoritmasının altın segment olarak isimlendirebilecek segmentteki müşterileri başarıyla tespit edebildiği gözlenmiştir.

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

Türkçe

Konular

-

Bölüm

Araştırma Makalesi

Yazarlar

Emrah Bilgiç
0000-0002-9875-2299
Türkiye

Yayımlanma Tarihi

30 Kasım 2019

Gönderilme Tarihi

2 Ocak 2019

Kabul Tarihi

1 Kasım 2019

Yayımlandığı Sayı

Yıl 1970 Cilt: 20 Sayı: 2

DOI

https://doi.org/10.37880/cumuiibf.507182

IZ

https://izlik.org/JA24XH22YR

Kaynak Göster

RIS / Bibtex

APA

Bilgiç, E. (2019). KARMA TİPTEKİ VERİLERİ KAMILA, K-ORTALAMALAR, KORTAYLAR ve K-PROTOTİPLER ALGORİTMALARIYLA KÜMELEME: KARŞILAŞTIRMALI BİR UYGULAMA. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 20(2), 48-70. https://doi.org/10.37880/cumuiibf.507182