A three-dimensional display for big data sets

Facing with high dimensional information in fields of Science, Technology and Commerce, users need effective visualization tools to find more useful information. For big data sets, it is very difficult to get useful information because the dimension is too large for a practical solution. This paper proposes a 3-D visualization method for big data sets. First of all, we employed the K-means clustering method to get the basic vectors. Then, we use these vectors to construct the reduction mapping. Finally, we get the three dimensional display for a sample point. To verify the feasibility of this method, we perform experiment on some well-known databases such as iris, wine and a large data set: Pendigits. The results are favorable. According to the 3-D display results, we can also get messages like classification, outliers, and classification level when given the level standards.