This project is about the dataset analysis of the market. Big market sales dataset Analysis is one of the key techniques used by large retailers to uncover associations between items. It works by looking for combinations of items that occur together frequently in transactions. To put it another way, it allows retailers to identify relationships between the items that people buy. We are given a large database of customer transactions.Each transaction consists of items purchase by a customer in a visit. We present an efcient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm. Big market sales analysis may provide the retailer with information to understand the purchase behavior of a buyer. This information will enable the retailer to understand the buyer's needs and rewrite the store's layout accordingly, develop cross-promotional programs, or even capture new buyers (much like the cross-selling concept). An apocryphal early illustrative example for this was when one supermarket chain discovered in its analysis that the customer that bought coffee powder often bought milk as well, have put the coffee powder close to milk coolers, and their sales increased dramatically.

This project is about the dataset analysis of the market. Big market sales dataset Analysis is one of the key techniques used by large retailers to uncover associations between items. It works by looking for combinations of items that occur together frequently in transactions. To put it another way, it allows retailers to identify relationships between the items that people buy. We are given a large database of customer transactions.Each transaction consists of items purchase by a customer in a visit. We present an efcient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm. Big market sales analysis may provide the retailer with information to understand the purchase behavior of a buyer. This information will enable the retailer to understand the buyer's needs and rewrite the store's layout accordingly, develop cross-promotional programs, or even capture new buyers (much like the cross-selling concept). An apocryphal early illustrative example for this was when one supermarket chain discovered in its analysis that the customer that bought coffee powder often bought milk as well, have put the coffee powder close to milk coolers, and their sales increased dramatically.