EVISTA – Interactive Visual Clustering System

Due to the enormous increase in the data, exploring and analyzing them is increasingly important but difficult to achieve. Information visualization and visual data mining can help to deal with this. Visual data exploration has a high potential and many applications such as fraud detection and data mining will use information visualization technology for an improved data analysis. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques which have been developed over the last decade to support the exploration of large data sets. VISTA is an interactive visual cluster rendering system which invites human into the clustering process, but there are some limitations in identifying the cluster distribution and human-computer interaction. In this paper, we propose an Enhanced VISTA (EVISTA) which addresses these drawbacks. EVISTA improves the visualization in two ways: first it uses the weighted vector normalization instead of max-min normalization, which improves the data visualization such that the user can understand the underlying pattern without human intervention. Secondly it completely eliminates the use of α tuning, which reduces the complexity in visual distance computation and eases the human computer interaction in a better way. The experiment results show that EVISTA explore the underlying pattern of the dataset effectively and reduces the user operation burden greatly.