MCHT: A maximal clique and hash table-based maximal prevalent co-location pattern mining algorithm

作者：

Highlights：

• A novel maximal prevalent co-location pattern mining framework is presented.

• The time and space costs are reduced efficiently by maximal cliques and hash tables.

• Enumerating maximal cliques is accelerated by bit string operations.

• The performance of the proposed method is proved by comparative experiments.

摘要

•A novel maximal prevalent co-location pattern mining framework is presented.•The time and space costs are reduced efficiently by maximal cliques and hash tables.•Enumerating maximal cliques is accelerated by bit string operations.•The performance of the proposed method is proved by comparative experiments.

论文关键词：Spatial data mining,Maximal co-location pattern,Maximal clique,Hash table

论文评审过程：Received 18 July 2020, Revised 3 December 2020, Accepted 1 March 2021, Available online 8 March 2021, Version of Record 26 March 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.114830