Finding frequent trajectories by clustering and sequential pattern mining

Data mining is a powerful emerging technology that helps to extract hidden information from a huge volume of historical data. Tills paper is concerned with finding the frequent trajectories of moving objects in spatia-temporal data by a novel method adopting the concepts of clustering and sequential pattern mining. The algorithms used logically split the trajectory span area into clusters and then apply the k-means algorithm over this clusters until the squared error minimizes. The new method applies the threshold to obtain active clusters and arranges them in descending order based on number of trajectories passing through. From these active clusters, inter cluster patterns are found by a sequential pattern mining technique. The process is repeated until all the active clusters are linked. The clusters thus linked in sequence are the frequent trajectories. A set of experiments conducted using real datasets shows that the proposed method is relatively five times better than the existing ones. A comparison is made with the results of other algorithms and their variation is analyzed by statistical methods. Further, tests of significance are conducted with ANOVA to find the efficient threshold value for the optimum plot of frequent trajectories. The results are analyzed and found to be superior than the existing ones. Tills approach may be of relevance in finding alternate paths in busy networks (congestion control) , finding the frequent paths of migratory birds, or even to predict the next level of pattern characteristics in case of time series data with minor alterations and finding the frequent path of balls in certain games.