Association Rule Mining on Customer's Data using Frequent Pattern Algorithm

Nowadays, organizations are generating immense amount of data dealing with multiple stakeholders. The collaborative environment and the use of latest technologies in the current market scenario, creating an additional pressure on the organization. The use of latest computing tools can help the enterprises to be competent, resourceful and to deal with huge data smartly. Therefore, this research shown the use of one of the promising computing strategies known as data mining Data mining is commonly known for generating hidden patterns and for knowledge discovery. This research proposed a model for analyzing the customer's data to generate hidden patterns from it. The purpose is to extract hidden knowledge from the data generated form multiple situations while dealing with the customers. The model implementation performed using association mining algorithm called FP-Growth. The algorithm is famous for generating association between multiple products purchased by different customers. The results generated bunch of rules, based on those rules organization can take future decisions. The proposed model can work for the organizations to support their business development plans using hidden knowledge.