Discretization of Multidimensional Web Data for Informative Dense Regions Discovery

Dense regions discovery is an important knowledge discovery process for finding distinct and meaningful patterns from given data. The challenge in dense regions discovery is how to find informative patterns from various types of data stored in structured or unstructured databases, such as mining user patterns from Web data. Therefore, novel approaches are needed to integrate and manage these multi-type data repositories to support new generation information management systems. In this paper, we first discuss and purpose several discretization methods which are suitable for multidimensional Web data. Based on it, we demonstrate some dense regions discovery applications by using Web usage data from a real Website. The experiments show that the discretization methods are quite effective and efficient, especially for highdimensional data. It also suggests that the discretization methods can be used in other practical Web applications, such as user patterns discovery.