Periodic pattern mining from spatio-temporal database using novel global pollination artificial fish swarm optimizer-based clustering and modified FP tree

The pattern of the object movement is periodic, but there occur few uncertainty issues during tracking and locating the movable object. Moreover, discovering the hidden periodic pattern from the historical moving object is a challenging task which may lead to an eventual periodic pattern. Therefore, a spatiotemporal database regarding probability is introduced to investigate the processing and also to detect probabilities concerning uncertainties. In this proposed approach, a periodic pattern tree miner is employed to find all eventual periodic patterns. In addition to this, a novel global pollination artificial fish swarm approach which is the integration of global flower pollination algorithm and artificial fish swarm algorithm is employed in the clustering process to obtain the best dense cluster. On the other hand, the novel global pollination artificial fish swarm approach provides a dynamic, high quality and an exact number of better clusters. We also presented the experimental analysis to obtain eventual periodic pattern mining by means of a modified frequent pattern tree, and the testing is done by utilizing the trucks and bus datasets. Then the comparative analysis is done with a sub-pattern tree to prove the memory usage and time consumption. The simulation results describe that the performances of global pollination artificial fish swarm approach provide better numerical efficiency on various 13 benchmark functions.