An improved diversity visualization system for multivariate data

Exploring and analyzing data is becoming increasingly difficult due to the growth of data. Visual analytics tools can be an attractive solution to support the process to derive insights from data. Currently, there are many visual representation methods to visualize the diversity in multivariate data sets. However, most of these applications focus on visual representation problems, and these solutions support limited interactive components for users to effectively explore and analyze data on screen. In this paper, the adaptive diversity table (ADT) is proposed to solve the visual representation problems (occlusion and technique interference). Furthermore, it integrates the mantra techniques to support users to accomplish seven important tasks (i.e. overview, zoom, filter, details-on-demand, relate, history, and extract) that are useful for high dimensional data exploration and data analysis. Experimental results show that the proposed ADT is a better visual representation as compared to other prior techniques. Majority of the respondents prefers to use the proposed ADT over the other visual representation methods. User studies also show that the proposed ADT is more useful as it enables the respondents to be more efficient in analyzing the data sets provided.