An efficient vertical-Apriori Mapreduce algorithm for frequent item-set mining

Algorithms such as OPUS and Apriori-based Mapreduce for enhancing the efficiency of mining frequent item-set for pattern recognition application from transactional dataset have been proposed in the literature. Most of these algorithms are, however, evaluated offline on relatively small data size. When confronting with larger data size, which is inevitable for todays organisation, most if not all algorithms performed not as efficient as required to meet the real time big data driven decision making needs. We therefore attempt to solve these efficiency problems by proposing a VAMR (Vertical-Apriori Map-reduce) algorithm. VAMR is based on data attribute identifier which is exploited as capability metric for mining frequency item-set from large dataset in a single node (for example in a single site enterprise) that has no distributed and parallel computing system environment. Our evaluations using synthetic datasets and data from public repository suggest that VAMR algorithm can offer superior efficiency in mining frequent item-sets from large transaction dataset.