A Parallel Frequent Itemsets Mining Algorithm Based on Binary Coding and Clustering under Cloud Environment

This paper proposes a parallel frequent itemsets mining algorithm based on binary coding under cloud environment.A special binary coding dependency calculating method is adopted to transfer the raw data and cluster based on dependency, then the data is distributed deployed in cloud environment and the parallel improved algorithm of FP-Growth based on shared multi-head table is used to mine frequent item sets.Experiments show that the algorithm performed nicely with large scale of data sets.