An improved matrix sorting index association rule data mining algorithm
Zhiping Zhou, Jiefeng Wang · 2014
Due to the existing Apriori association rules data mining algorithms require to scan the database many times and generate a large numbers of candidate sets, which produce giant I/O expense issues, result in low data mining computational efficiency. Matrix algorithms can improve the efficiency in computing frequency 2-itemset, but not delete non-frequency item set before calculation, not effectively improved efficiency. A matrix-based and sorting index association rules algorithm is proposed. Firstly, delete the unwanted affairs and items, the frequent binomial set obtained by matrix multiplying and search table, combined with sorting index derived the rest of the frequency k-itemsets. Compared with Apriori algorithm and matrix algorithm, the proposed algorithm scan database only once, which can directly find the frequency k-itemsets, especially when frequent item sets are higher or need to have a date mining update, the algorithm has higher efficiency and feasibility. Experiment shows that proposed matrix sorting index algorithm greatly improved the data mining efficiency and scalability.