Grade-based Spatio-Temporal Sequential Pattern Mining using Support and Event Index Measures

Abstract

Objectives: The knowledge on cause-effect relationships between instances of real-world entities can be gathered by extracting sequential patterns from spatio-temporal databases. The discovery of the patterns in the context of space and time is a challenging issue. The sequential pattern mining algorithms designed for traditional databases may result in the loss of spatio-temporal correlations due to the improper estimations of properties related to the time and space. The proposed work approaches the problem of designing sequential pattern mining algorithm specifically for spatio-temporal event datasets. Methods/Statistical Analysis: An algorithm is proposed which is based on frequency-based measures for mining frequent spatio-temporal sequential patterns. The spatio-temporal sequential pattern mining based on Support index and Event index algorithm proposes two new parameters support index and event index which are used to scrutinize the sequences extracted from the database. A data structure is also proposed to represent the spatio-temporal data for efficient pattern mining. Findings: The proposed algorithm generates the interesting set of frequent sequential patterns. The proposed algorithm is compared with Slicing-STS-Miner and MST-ITP and the experimental results proved that the proposed algorithm performs well with the order of two to three. Application/Improvements: The proposed algorithm uses frequency-based measures rather than density-based measures. Frequency-based measures take less computational time when compared to density-based measures. The proposed technique is suitable for extracting knowledge in the form of sequential patterns from spatio-temporal point databases.