Mining Frequent and Correlated Items Using Map Reduce Framework and Tree Data Structure
Big data has a characteristics like large like volume, velocity, variability and complexity value. Big data mining is the capability of extracting useful information from these large datasets or streams of data. The combinatorial explosion of FIM methods becomes even more problematic when they are applied to big data fortunately; recent improvements in the field of parallel programming already provide good tools to tackle this problem. However, these tools come with their own technical challenges, e.g. balanced data distribution and inter-communication costs.