Mining fuzzy association rules using MapReduce technique

Mining association rules from large databases is one of the most important tasks from data mining. Nowadays, the majority of companies produce a significant amount of data stored in distributed databases. In this case, most of the traditional algorithms for mining association rules become ineffective because they require a lot of resources to extract the frequent patterns. The cloud computing technologies provide us the infrastructure for handling such massive datasets. In this paper, we propose an extension of the Count Distribution algorithm for mining fuzzy association rules from a distributed database. The algorithm uses the MapReduce programming model, which aims to distribute the mining process over many cluster nodes. Distributing the mining process allows handling very large databases and significantly improves the execution time.