R Programlama ile Birliktelik Kuralları Analizi: Tüketicilerin İnternet Üzerinden Yaptıkları Alışveriş Verisinin Apriori ve Eclat Algoritmalarıyla İncelenmesi

Abstract

Data mining is the process of extracting meaningful information from big data sets. Data mining technique, which is widely used, has been applied to an e-commerce website in this study. In this study, it is aimed to obtain the profiles of people who shop online and to determine which e-commerce sites they use together. For this purpose, Association rules analysis is applied. Support, confidence and lift ratios and ranking of both web sites and product categories that consumers make purchases are performed. R programming language is used in the application and the results of the analysis are obtained with Apriori and Eclat algorithms.

Analysis of Health Indicators by Association Rules

“…Süreç yinelenerek sıklıkla bulunan elemanların olduğu öğe aday kümesini oluşturamayana kadar devam eder. Bu süreçte birleştirme ve budama adı verilen işlemler uygulanır ve sonuca ulaşılır [24]. Apriori algoritması veri büyüklüğüne göre daha iyi performans sergilemesi hem de paralel algoritmaların temelini oluşturması sebebiyle tercih edilir [25].…”

A Combined Approach for Customer Profiling in Video on Demand Services Using Clustering and Association Rule Mining

“…Apriori reveals the relationships among different components and, hence, it is applied in many domains such as retail shopping, credit card transactions, telecommunication service purchases, banking, insurance, and health care -in the latter case, in the form of patient histories [49]. It has three main measures: support, confidence and lift; where support is an indication of how frequently the data appears in the given dataset, confidence is the measure of how often the rule has been found to be true, and lastly lift is a correlation analysis measure to identify the relation between antecedent and consequent [50].…”