Abstract

The volume of Big data is increasing in gigabytes day by day which are hard to make sense and difficult to analyze. The challenges of big data are capturing, storing, searching, sharing, analysis and visualization of these datasets. Big data leads to clutter in their visualization. Clutter is a crowded or disordered collection of graphical entities in information visualization. It can blur the structure of data. In this paper, we present the concept of clutter based dimension reduction. Our purpose is to reduce clutter without reducing information content or disturb data in any way. Dimension reduction is a technique that can significantly reduce the dimensions of the datasets. Dimensionality reduction is useful in visualizing data, discovering a compact representation, decreasing computational processing time and addressing the curse of dimensionality of high-dimensional spaces.

Authors and Affiliations