SN-RNSP: Mining self-adaptive nonoverlapping repetitive negative sequential patterns in transaction sequences

Abstract

Negative sequential patterns (NSP) focus on non-occurring events and play a role that cannot be replaced by positive sequential patterns (PSP). Considering the repetitive occurrence of sequential patterns in a sequence, repetitive NSP (RNSP) mining captures frequent NSP across different sequences from a database. Those patterns benefit many tasks of transaction services, e.g., fraud detection and medical diagnosis. However, limited studies focusing on mining RNSP are proposed, e.g., e-RNSP and ONP-Miner, and they are devised under strict constraints and are inefficient in practice. To address these issues, this paper proposes a Self-adaptive Nonoverlapping RNSP mining method SN-RNSP to mine nonoverlapping RNSP with the self-adaptive gap between successive elements from transaction sequences, which requires that each element cannot be reused at the same position in occurrences, and the gap value does not need to be specified in advance. First, this paper develops a method that maintains occurrences of pattern candidates via the bitmap structure to capture all repetitive PSP (RPSP), which utilizes the bitmap-based operation to calculate support efficiently. Second, SN-RNSP leverages bitmaps to record the locations of RPSP and RNSP in the database and query the repetition times of corresponding RPSP for the support calculation of RNSP. Conducted on real-world and synthetic datasets, extensive experiments demonstrate that SN-RNSP can discover more patterns with better mining performance than the state-of-the-art RNSP mining algorithms in transaction sequence databases.