An efficient parallel row enumerated algorithm for mining frequent colossal closed itemsets from high dimensional datasets

Mining colossal itemsets from high dimensional datasets have gained focus in recent times. The conventional algorithms expend most of the time in mining small and mid-sized item sets, which do not enclose valuable and complete information for decision making. Mining Frequent Colossal Closed Itemsets (FCCI) from a high dimensional dataset play a highly significant role in decision making for many applications, especially in the field of bioinformatics. To mine FCCI from a high dimensional dataset, the existing preprocessing techniques fail to prune the complete set of irrelevant features and irrelevant rows. Besides, the state-of-the-art algorithms for the same are sequential and computationally expensive. The proposed work highlights an Effective Improved Parallel Preprocessing (EIPP) technique to prune the complete set of irrelevant features and irrelevant rows from high dimensional dataset and a novel efficient Parallel Frequent Colossal Closed Itemset Mining (PFCCIM) algorithm. Further, the PFCCIM algorithm is integrated with a novel Rowset Cardinality Table (RCT), an efficient method to check the closeness of a rowset and also an efficient pruning strategy to cut down the mining search space. The proposed PFCCIM algorithm is the first parallel algorithm to mine FCCI from a high dimensional dataset. The performance study shows the improved effectiveness of the proposed EIPP technique over the existing preprocessing techniques and the improved efficiency of the proposed PFCCIM algorithm over the existing algorithms. (C) 2018 Elsevier Inc. All rights reserved.