Implementation of Sales Analysis System for Fancy Shop based on Association Rule Mining

Abstract

One of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. The most time consuming operation in this discovery process is the computation of the frequency of the occurrences of interesting subset of items (called candidates) in the database of transactions. The proposed system presents the FP-Growth algorithm to avoid or reduce candidate generation. FP-growth method is efficient and scalable for mining both long and short frequent patterns without candidate generation. It not only heirs all the advantages in the FP-growth method, but also avoid its bottleneck in database size dependence when constructing the frequent pattern tree (FP-tree).It greatly reduces the need to traverse the database. This process analyzes customer buying habits by finding associations between the different items that customers place in their shopping baskets. The proposed system tends to analysis on the Sales System of Fancy Shop using FP-growth algorithm under association rules mining. The system evaluates more suitable products to sell and how to display them according to their purchasing products.