Store Basket Analysis using Data Mining Algor...

Store Basket Analysis using Data Mining Algorithms

Author Name : Neha Farath, Shaik Rizwana, Amina Firdouse

ABSTRACT

Data mining is a fast-growing field in a variety of fields, including remote sensing, geographic information systems, computer mapping, environmental assessment, and planning. People's capacity to interpret the data gathered significantly outnumbers their ability to collect it. In order to extract knowledge from massive geographic databases, innovative and efficient approaches are required. The application of attribute data mining approaches in data mining was broadened. Many people have recently begun purchasing items from internet e-commerce sites. Also, most of them may have observed an up-sell function called 'often bought along' in most of these sites, such as Amazon, which forecasts what all goods will go along with the item you just placed in the basket. Customers can choose to add all of the products displayed in this feature to their basket or just the ones they need. Market Basket Analysis refers to the complete process of studying clients' purchase habits. We will evaluate Eclat and Fp growth algorithms in this project to achieve the best results on basket analysis in a store based on client interests and frequent purchases. Eclat is a vertical database layout approach for mining frequent item sets, whereas FP-growth is an upgraded version of the Apriori Algorithm for mining frequent patterns.

Keywords— apriori, eclat, fp-growth, frequent itemsets, association rules.