An improved algorithm for frequent sequence pattern mining based on PrefixSpan-ComplexPrefixSpan
Year: 2023
Type: conference-paper
Abstract: In order to solve the problem that the sequence pattern mining algorithm requires a large computation time, an improved PrefixSpan algorithm is proposed. Because PrefixSpan is an algorithm that does not need to produce candidate sequences, and it has a lower temporal complexity and spatial complexity compared to other sequence pattern mining algorithms. However, the traditional PrefixSpan algorithm is only applicable to mining frequent patterns in single sequences. This paper introduces a multi-sequence frequent pattern mining framework, applied to the medical data set synthesized by Synthea generator. The results show that the algorithm has certain feasibility and has certain theoretical and practical significance for the study of multi-sequence frequent pattern mining.
Authors: Shiyong Ning, Shihui Zhang
Institution: Harbin University of Commerce
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4398234031