Determining parameters of DBSCAN Algorithm in Dynamic Environments Automatically using Dynamic Multi-objective Genetic Algorithm

The spatio-temporal (ST) clustering is a relatively new field in data mining with a great popularity, especially in the geographic information. The moving objects are a type of ST data where the available information on these objects includes their last position. The strategy of performing the clustering operation on all-time sequences is used for clustering the moving objects. The problem with density-based clustering, which uses this strategy, is that the density of clusters may change at any point in time due to the displacement of points. Hence, the input parameters of an algorithm like DBSCAN used to cluster the moving objects will change, and have to be determined again. The DBSCAN-based methods have been proposed so far, assuming that the value of the input parameters is fixed over time and does not provide a solution for their automatic determination. Nonetheless, with the objects moving and the density of the clusters changing, these parameters have to be determined appropriately again at each time interval. This work uses a dynamic multi-objective genetic algorithm in order to determine the parameters of the DBSCAN algorithm dynamically and automatically to solve this problem. The proposed algorithm in each time interval uses the clustering information of the previous time interval to determine the parameters. The Beijing traffic control data is used as a moving dataset in order to evaluate the proposed algorithm. The experiments show that using the proposed algorithm for dynamic determination of the DBSCAN input parameters outperforms DBSCAN with fixed input parameters over time in terms of the Silhouette and Outlier indices.