Analysis of Data Mining Algorithms in Market Basket Analysis

Data mining is the process of sorting massive volumes of data to uncover patterns and trends that could be utilized in data analysis to help solve business problems. Due to data mining skills and equipment, businesses can help in predicting and making more informed decisions. This paper has described several aspects of data mining and data mining technology. Market basket analysis (MBA) is one of the main applications of the data mining environment that this study primarily focuses on. Marketers frequently utilise MBA, a type of analytical technique, to comprehend the purchasing habits of their customers. The goal of the MBA is to find the similarities between similar products that are bought simultaneously as well as the co-occurring patterns. The association rule technique is used to do this. By identifying the commodities that buyers commonly combine, MBA helps businesses increase their sales and marketing strategies. Additionally, this study concentrates on distinct techniques for examining particular data mining trends. Analysis of the Apriori and Frequent Pattern (FP) Growth techniques performance revealed that 10000 transactions were required to analyze completion time, and a total of 5% support was required to examine Apriori and FP-Growth with a time reduction of 65%.