Discovering high utility itemset using MapReduce
Wei Guo Song, Jiapei Xu · 2016
Based on the MapReduce framework, we propose HUIMR algorithm on discovering high utility itemset (HUI). The HUIMR algorithm consists of counting and mining two stages. For the counting stage, MapReduce is used to calculate high transaction-weighted utilization items. While during the mining stage, high transaction-weighted utilization itemset tree is defined at first; then based on the pattern growth strategy, MapReduce is exploited for parallel mining HUIs. We tested HUIMR algorithm on efficiency and speedup.