Implementation of Data Mining Using Association Rules for Transactional Data Analysis - Muhamad Brilliant; Dwi Handoko; Sriyanto - (Proceeding International Conference on Information Technology and Business, 2017)
Data is an important property for everyone. Large amount of data is available in the world. There are various repositories to store the data into data warehouses, databases, information repository, etc. This large amount of data needs to process so that we can get useful information. Data mining is a technique to get information that hidden from collections of data. There are several major functions in data mining such as estimation, prediction, classification, clustering and association.This research use association rule to find the interconnections of the association between the data items in data transaction. The technique used to find the ruleis the FP-Growth. FP-Growth is one of the algorithms used to find frequent item sets in the set of transaction data.This study aims to create a simulation using a data mining association rule with the FP - growth algorithm as a reference to determine a list of product packages that offered to consumers. Testing that has been done based on the results of functional testing with black box method, it can be concluded that by implementing data mining with association rule method can help the company in finding consumer pattern. It is expected that the company can create a list of entertainment service, product packages that can be offered to consumers based on the rules generated at competitive pricesKeywords: Association Rule, Data Mining, FP-Growth