HU-RNSP: Efficiently mining high-utility repeated negative sequential patterns

Abstract

High-utility repeated negative sequential patterns (HURNSPs) mining plays a key role in behavioral analysis and user preference mining. However, existing HUSPM mining methods do not consider the importance of repeated negative sequential patterns (RNSPs) or high-utility negative sequential patterns (HUNSPs), which pose the following challenges for HURNSPs mining: (1) Lack of an effective method for calculating the utility of high-utility repeated positive sequential patterns (HURPSPs), (2) Lack of an effective method for calculating the utility value of high-utility repeated negative sequential candidate patterns (HURNSCs). To solve the above challenges, this paper proposes an effective algorithm, HU-RNSP, for mining HURNSPs. First, an algorithm, called HURSpan, is proposed to mine HURPSPs by integrating RNSP and HUSPM into the mining of HURNSPs. Second, an algorithm, NSPGwl, is proposed, which converts HURPSPs into HURNSCs, effectively calculates the utility of HURNSCs, and compares the utility of HURNSCs with a minimum utility threshold to obtain HURNSPs. Experimental results on nine datasets demonstrate that HU-RNSP is more effective than baseline methods in discovering HURNSPs. Additionally, we analyze the impact of data features on HURNSP mining. The results indicate that HU-RNSP demonstrates strong adaptability and computational efficiency across experiments on datasets with varying data factors.