Pattern of E-marketplace Customer Shopping Behavior using Tabu Search and FP-Growth Algorithm

Ayu Meida,Dian Palupi Rini,Sukemi Sukemi

doi:10.11591/ijeei.v7i4.1362

Abstract

Pattern of customer shopping behavior can be known by analyzing market cart. This analysis is performed using Association Rule Mining (ARM) method in order to improve cross-sale. The weakness of ARM is if processed data is big data, it takes more time to process the data. To optimize the ARM, we perform merging algorithm with Improved Tabu Search (TS). The application of Improved TS algorithm as optimization algorithm for preprocessing datasets, data filtering, and sorting data closely related products on sales data can optimize the ARM processing. The method of Association Rule Mining (FP-Growth) to determine frequent K-itemset, Support value and Confidence value of data which is already sorted on TS is based on patterns which often appear in the dataset so it generates rules as reference of decision making for company. To measure the level of power of rule which has been formed, the Lift Ratio value was calculated. Based on the calculation of 97 rules produced, the lift ratio produces values > 1 of 82.54% and based on processing time, it produces the fastest data search in 1.66 seconds. When compared with previous research that uses the hybrid method, for data retrieval based on processing time, it produces the fastest data search within 12.3406 seconds, 150 seconds and 50 seconds. Previous studies have only compared the processing time of data searching without regard to validation / accuracy of data search. The test results in this study obtained more optimal results than when compared with the results of previous studies, namely in time efficiency and data mining in real time and more accurate data validation. As a conclusion, the resulting rule can be used as a reference in understanding shopping behavior patterns customer on the E-Marketplace.

Highlights

Customer is core element of any business model
The method of Association Rule Mining (FPGrowth) to determine frequent K-itemset, Support value and Confidence value of data which is already sorted on Tabu Search (TS) is based on patterns which often appear in the dataset so it generates rules as reference of decision making for company
Association Rule Mining is one of the market cart analysis method which have strong relationship with study database on customer transaction data to figure out pattern of the customer shopping behavior, so it can be known that the products purchased at the same time [18]

Summary

Introduction

Customer is core element of any business model. Customer satisfaction is becoming the most important factor to support the success of a company. Every customer has different scheme in order to meet their necessity, which will affect their decision when buying a product. A group of customers still have some similarities of shopping behavior pattern in consuming product to maximize their satisfaction [1, 2]. The correlation between product’s type that purchased by many customers can be analyzed to configure the pattern of customer shopping behavior [3]. Customer shopping behavior pattern can be known using market cart analysis method [4]. Association rule mining is one of popular market cart analysis method that used customer shopping behavior. The problem of behaviors can be minimized, so it can improve cross-sales [5]

Methods

Results

Conclusion