spatial temporal crime analysis in vechical crime

The aim of the clustering research is to identify subgroups of instance in a vehicle crime density. In this research, we implement a two-step clustering algorithm which is well-suited when we deal with a large dataset. It combines the ability of the K-Means clustering to handle a very large dataset, and the ability of the subspace Hierarchical clustering to give a visual presentation of the results called “Histogram”. This one describes the clustering process, starting from rough clusters, until the whole dataset belongs to one cluster. It is especially helpful when we want to detect the appropriate number of clusters. We perform the clustering algorithm on the hidden variables supplied by a PCA computed from the original variables.