Survey on Frequent Item-Set Mining Approaches in Market Basket Analysis

Market basket analysis is one of the important method generally used in data mining. It analyzes customer purchasing behavior. It is a process to find relationship or association between different items that are placed in the customer basket. Analyze the data available into the supermarket's database which is helpful in locating the product on shelves. To make business intelligence market basket analysis is well-known activity. It is the process of finding frequent item-sets which are adjacent to each other. Frequent itemsets are identified by an apriori algorithm. Association rule is used to find an association between frequent items. Sales of certain products decrease when a product is rearranged in the shelves. It identifies purchasing pattern of items. Market Basket Analysis is based on the customer behavior. It analyzes arrangement of items on retailer shops. All items which are related to one another must be placed one after another which is helpful to the customer to buy products. It is a well-known business problem which can be solved by using association rules mined from transaction data to maximize customer behavior. It assigns multiple different features such as customer satisfaction, time-saving, data analysis, customer behavior analysis, customer relationship management and increases the sale of products.