Triangular kernel nearest neighbor based clustering for pattern extraction in spatio-temporal database

To date, various fields of applications have utilized spatio-temporal databases not only to store data, but to support decision making. For example, in traffic accident analysis; it is required to have knowledge on the pattern of accidents resulting in death. Thus, in such analysis, clustering technique is desired to implement pattern extraction. This paper presents clustering of spatio-temporal database using kernel nearest neighbor approach. It is chosen due to its ability to determine the number of clusters automatically. There are various types of kernel functions exist in the literatures, but the issue of concern is how to determine an appropriate kernel function for this application. In this study, two commonly used kernel functions, namely Gaussian and triangular, are investigated. From various experiments conducted, both functions produce reasonable clusters, but the triangular kernel nearest neighbor based clustering (TKNN) provides better performance with smaller number of iteration compared to Gaussian kernel nearest neighbor based clustering (ILGC) and K-means. Thus, TKNN is good option in clustering spatio-temporal database.