Knowledge Discovery in Road Accidents Database Integration of Visual and Automatic Data Mining Methods
Imad Abugessaisa · 2008
Road accident statistics are collected and used by a large number of users and this can result in a huge volume of data which requires to be explored in order to ascertain the hidden knowledge. Potential knowledge may be hidden because of the accumulation of data, which limits the exploration task for the road safety expert and, hence, reduces the utilization of the database. In order to assist in solving these problems, this paper explores Automatic and Visual Data Mining (VDM) methods. The main purpose is to study VDM methods and their applicability to knowledge discovery in a road accident databases. The basic feature of VDM is to involve the user in the exploration process. VDM uses direct interactive methods to allow the user to obtain an insight into and recognize different patterns in the dataset. In this paper, I apply a range of methods and techniques, including a paradigm for VDM, exploratory data analysis, and clustering methods, such as K-means algorithms, hierarchical agglomerative clustering (HAC), classification trees, and self-organized-maps (SOM). These methods assist in integrating VDM with automatic data mining algorithms. Open source VDM tools offering