Flexible information visualization of multivariate data from biological sequence similarity searches
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In a survey, when participants are allowed to give multiple response answers, the results will be presented in patterns of clustering based on similarity factors. Multidimensional scaling (MDS) is often used to reduce the dimension of data for presenting information in clusters that allow results to be interpreted according to the survey subjects and attributes. However, too many subjects and attributes will produce massive output points (coordinates) in the results that provide difficulties in the presentation. In this paper, we propose a tool called Murvis (Multiple Response Visualization) to provide users (researchers) to visualize the MDS output coordinates in 2D and 3D space with flexible views manipulation, and results reclassification based on colored attributes. We use Java programming language to read all the MDS output coordinates and apply a distance ratio algorithm to visualize the output points in height. At the same time, the Java program also reads all coordinates for MDS output attributes to assign colours to any particular attribute analysis. As a case study, we work on 50 data coordinates and perform the testing on two more other datasets. The first dataset consists of 200 cases to look into the ease of visualization technique. The second dataset has different subjects and cases to test on the flexibility of the tool to reclassify the attribute results with colours. Our study benefits the researchers or statisticians for analyzing their findings for multiple response answers.