Datasets which are identical over a number of statistical properties, yet produce dissimilar graphs, are frequently used to illustrate the importance of graphical representations when exploring data. This paper presents a novel method for generating such datasets, along with several examples. Our technique varies from previous approaches in that new datasets are iteratively generated from a seed dataset through random perturbations of individual data points, and can be directed towards a desired outcome through a simulated annealing optimization strategy.
Visualization is a technique to graphically represent sets of data. When data is large or abstract, visualization can help make the data easier to read or understand. There are visualization tools for search, music, networks, online communities, and almost anything else you can think of. Whether you want a desktop application or a web-based tool, there are many specific tools are available on the web that let you visualize all kinds of data. Here are some of the best:
The burgeoning interest in R demonstrates that there’s demand for analytics to solve real, business-critical problems in a broad spectrum of companies and roles, and that some of the incumbent analytics offerings, in particular SAS and SPSS, don’t sufficiently meet the growing need for analytics in many major companies. Annotated link http://www.diigo.com/bookmark/http%3A%2F%2Fspotfire.tibco.com%2Fcommunity%2Fblogs%2Fenterpriseanalytics%2Farchive%2F2009%2F01%2F08%2Fanalytics-in-the-nyt.aspx
N. Padhy, D. Mishra, and R. Panigrahi. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 2 (3):
43-58(June 2012)