Visualization Techniques

Visualization is considered to be one of the valuable tools in data mining. Visual analysis helps to understand data with minimum effort. Using graphs/ 3D plots to visualize data is more effective way of understanding massive sets of data. Ofcourse, we have other ways of exploring data like for example using statistical functions namely mean which depicts the average value, standard deviation / variance which depicts the spread of data and correlation which depicts the relationship between attributes. Some of the classical visualization techniques include

(i) Histograms (ii) Scatter plots (iii) Pie charts

              A frequency histogram displays the distribution of values for attributes by dividing the possible values into bins and showing the number of objects/records falling in each bin. Scatter plot is a great way to visualize paired numeric attributes. For example, you have two attributes height and weight. Scatter plot can visually represent the correlation of height wrt weight. It may indicate facts like “As the height increases, weight also increases” or “As the height increases, weight decreases”. Data mining techniques use scatter plots to identify redundant attributes which can be dropped from analysis.
Newer visualization techniques are evolving with growing business needs and need to minimize efforts for decision making. Scientists use visual analysis to explore previously unknown patterns in their research/simulation data. Hence visualization has gained wide acceptance across all spheres of life. Some visualization techniques which I really admire about are the geo-spatial visualization and word clouds.

• Geo Spatial Data: Consider the average energy consumption per person data for various regions of the world. In this scenario, it is more meaningful to visualize the energy data against the geography to get some quick facts about data. The figure below shows the visualization. The bigger the circles and the darker the shade, indicates that energy consumption is high. On contrary, lighter shade with smaller circles indicate relatively low energy consumption.
  

  geo spatial Data Analysis : Energy consumption data

 

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• Word Cloud Analysis: I am sure, you must have seen this visualization when you visited any site like torrentz, pdf-geni, rapidsearch,etc. A word cloud indicates the frequency of word usage as a function of font/ color of the text. Bigger the font and darker the shade, the more frequent it appears in a given data set. The figure below shows the word cloud analysis of Lincoln’s speech. As seen from the visual, Lincoln more frequently used words like people, government, constitution, etc as these are relatively bigger in font. The figure on the right shows the word cloud analysis for this blog. There are some online tools to get the word cloud constructed for you blog/document – http://tagcrowd.com/

Word Cloud Visualization

Word cloud analysis of this blog

There is a classical example where visualization technique helped identify the reason for cholera outbreak in London. On visualizing the chorela affected houses, it was evident that those people nearer to a pond had developed cholera and those away from this pond had lesser probability. On inspection of the water samples collected from the pond, it showed contamination. Hence using visualization a major problem was solved.
In a recent article published in leading magazine, i had learnt that expert systems(AI) are visually repesenting the probable reason for health problem by highlighting the affected organ(s) in 3 dimensions with ability to drill down to the microscopic levels( based on medical test data). All these facts, prove visualization to be effective tools.
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• AI
• correlation
• expert systems
• scatter plot
• Visualization
• word cloud