Efficient mining of incremental high utility patterns with negative unit profits over all the accumulated stream data

Abstract

Traditional high utility pattern mining had considered that items in databases have positive unit profits, but considering negative unit profits is often required in real life. Thus, many algorithms considering both positive and negative unit profits have been proposed in static data environments. Meanwhile, one of the most important parts of data analysis is how to handle the accumulated stream data in real-world systems. However, existing methods considering negative unit profits in a static environment are inadequate for processing data streams, as they require repeated data access, incurring additional resources with multiple data scans. This paper suggests an effective method considering positive and negative unit profits and dynamic databases for high utility stream pattern mining. To avoid storing data in memory and scanning it multiple times, the proposed approach constructs its data structure by performing a single scan of the incremental data without storing it in the memory. Then, through a reconstruction process, it efficiently integrates and manages the new data while optimally maintaining the structures. This methodology enables efficient mining without the loss of significant patterns. Experiments with real and synthetic datasets show that the proposed approach has improved performance to state-of-the-art methods, including adjusted approaches, regarding runtime, memory usage, and scalability. In addition, the proposed method demonstrates enhanced performance than the baseline method in terms of the resources of each process and the number of incremental databases. Further statistical evaluation of the accuracy test shows that the proposed method extracts results without pattern loss or duplication.