RWFIM: Recent weighted-frequent itemsets mining

In recent years, weighted frequent itemsets mining (WFIM) has become a critical issue of data mining, which can be used to discover more useful and interesting patterns in real-world applications instead of the traditional frequent itemsets mining. Many algorithms have been developed to find weighted frequent itemsets (WFIs) without time-sensitive consideration. The discovered out-of-date information may, however, be meaningless and useless in decision making. In this paper, a novel framework, namely recent weighted-frequent itemsets mining (RWFIM) is proposed to concern both the weight and time-sensitive constraints. A projected-based RWFIM-P algorithm is first proposed for mining the designed recent weighted-frequent itemsets (RWFIs) with weight and time-sensitive consideration. It uses the projection-and-test mechanism to discover RWFIs in a recursive way. Based on the developed RWFIM-P algorithm, the entire database can be projected and divided into several sub-databases according to the currently processed itemset, thus reducing the computational costs and memory requirements. The second RWFIM-PE algorithm is also proposed to improve the performance of the first RWFIM-P algorithm based on the developed Estimated Weight of 2-itemset Pruning (EW2P) strategy to mine the RWFIs without generating the unpromising candidates, thus avoiding the computations of the projection mechanism compared to the first RWFIM-P algorithm. Experiments are conducted to evaluate the performance of the proposed two algorithms compared to the traditional WFIM in terms of execution time, number of generated RWFIs and scalability under varied two minimum thresholds in several real-world and synthetic datasets.