Clustering of tourist routes for individual tourists using sequential pattern mining

Abstract

Grouping individual tourists who have the same or similar tourist routes over the same time period makes it more convenient for the tourists at a low cost by providing transportation means such as regular or occasional tour buses, driver, and tourism guides. In this paper, we propose a mathematical formulation for the tour routes clustering problem and two phases for a sequential pattern algorithm for clustering similar or identical routes according to the tourist routes of individual tourists, with illustrative examples. The first phase is to construct a site by site frequency matrix and prune infrequent tour route patterns from the matrix. The second phase is to perform clustering of the tour routes to determine the tour route using a sequential pattern mining algorithm. We compare and evaluate the performance of our algorithms, i.e., in terms of execution time and memory used. The proposed algorithm is efficient in both runtime and memory usage for the increasing number of transactions.