Research on improved Apriori algorithm based on MapReduce and HBase
Dongyu Feng, Ligu Zhu, Lei Zhang · 2016
In order to improve the efficiency of Apriori algorithm for mining frequent item sets, MH-Apriori algorithm was designed for big data to address the poor efficiency problem. MH-Apriori takes advantages of MapReduce and HBase together to optimize Apriori algorithm. Compared with the improved Apriori algorithm simply based on MapReduce framework, timestamp of HBase is utilized in this algorithm to avoid generating a large number of key/value pairs. It saves the pattern matching time and scans the database only once. Also, to obtain transaction marks automatically, transaction mark column is added to set list for computing support numbers. MH-Apriori was executed on Hadoop platform. The experimental results show that MH-Apriori has higher efficiency and scalability.