Negative pattern discovery with individual support

Negative sequential pattern (NSP) discovery is crucial, and sometimes it carries more enlightening information than positive sequential pattern (PSP) mining in data science. Owing to its computational complexity and exponential search space, the task of discovering NSPs is often more difficult and challenging than that for PSPs. To date, a few NSP mining algorithms have been proposed. Particularly, most algorithms only consider a single support for mining, thus they cannot present good results in many special real-world applications. To solve this problem and achieve better efficiency on a long sequence database or a large-scale database, we propose a novel algorithm called Negative Sequential Patterns with Individual Support (NSPIS) in this paper. The projection mechanism is adopted to NSPIS, which allows greatly reduce the search space and simultaneously improve the efficiency. Finally, detailed results of the experiments show that NSPIS can achieve better performance, and it costs less memory on large-scale datasets compared to the state-of-the-art algorithm. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

The discovery of negative sequential patterns (NSPs) is crucial in data science, as it often provides more enlightening information than positive sequential patterns (PSPs). However, the task of discovering NSPs is more difficult and challenging due to computational complexity and a large search space. This paper proposes a novel algorithm called Negative Sequential Patterns with Individual Support (NSPIS) to solve this problem and achieve better efficiency.