UHUOPM: High Utility Occupancy Pattern Mining in Uncertain Data

It is widely known that there is a lot of useful information hidden in big data, and it is prevalent for individuals to mine crucial information for utilization in many real-world applications. To find patterns that can represent the supporting transaction, a recent study was conducted to mine high-utility occupancy patterns whose contribution to the utility of the entire transaction is greater than a certain value. Moreover, in realistic applications, patterns may not exist in transactions but be connected to an existence probability. In this paper, a novel algorithm, called High Utility-Occupancy Pattern Mining in Uncertain databases (UHUOPM), is proposed. The patterns found by this algorithm are called Potential High Utility Occupancy Patterns (PHUOPs). To reduce memory cost and time consumption and to prune the search space in the algorithm as mentioned above, probability-utility-occupancy list (PUO-list) and probability-frequency-utility table (PFU-table) are used. Finally, substantial experiments were conducted to evaluate the performance of proposed UHUOPM algorithm on both real-life and synthetic datasets.