Question: Explain different visualization techniques that can be used in data mining.
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Mumbai University > Information Technology > Sem6 > Data Mining and Business Intelligence

Marks: 10M

Year: May2015, Dec2015

 modified 10 months ago by Abhishek Tiwari ♦♦ 50 written 3.4 years ago by
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• Data visualization aims to communicate data clearly and effectively through graphical representation.
• Data visualization has been used extensively in many applications for Eg. At work for reporting managing business operations and tracking progress of tasks.
• More popularly, we can take advantage of visualization techniques to discover data relationships that are otherwise not easily observable by looking at the raw data.

Data Visualization techniques:

1.Pixel oriented visualization techniques:

• A simple way to visualize the value of a dimension is to use a pixel where the color of the pixel reflects the dimension’s value.
• For a data set of m dimensions pixel oriented techniques create m windows on the screen, one for each dimension.
• The m dimension values of a record are mapped to m pixels at the corresponding
• position in the windows.
• The color of the pixel reflects other corresponding values.
• Inside a window, the data values are arranged in some global order shared by all windows
• Eg: All Electronics maintains a customer information table, which consists of 4 dimensions: income, credit_limit, transaction_volume and age. We analyze the correlation between income and other attributes by visualization.
• We sort all customers in income in ascending order and use this order to layout the customer data in the 4 visualization windows as shown in fig.
• The pixel colors are chosen so that the smaller the value, the lighter the shading.
• Using pixel based visualization we can easily observe that credit_limit increases as income increases customer whose income is in the middle range are more likely to purchase more from All Electronics, these is no clear correlation between income and age.

Fig: Pixel oriented visualization of 4 attributes by sorting all customers in income Ascending order.

2.Geometric Projection visualization techniques

• A drawback of pixel-oriented visualization techniques is that they cannot help us much in understanding the distribution of data in a multidimensional space.
• Geometric projection techniques help users find interesting projections of multidimensional data sets.
• A scatter plot displays 2-D data point using Cartesian co-ordinates. A third dimension can be added using different colors of shapes to represent different data points.
• Eg. Where x and y are two spatial attributes and the third dimension is represented by different shapes
• Through this visualization, we can see that points of types “+” &”X” tend to be collocated.

Fig: visualization of 2D data set using scatter plot

3.Icon based visualization techniques:-

• It uses small icons to represent multidimensional data values
• 2 popular icon based techniques:-

3.1 Chern off faces: - They display multidimensional data of up to 18 variables as a cartoon human face.

Fig: chern off faces each face represents an ‘n’ dimensional data points (n<18)

$\hspace{1 cm}$ 3.2 Stick figures: It maps multidimensional data to five –piece stick figure, where each figure has 4 limbs and a body.

• 2 dimensions are mapped to the display axes and the remaining dimensions are mapped to the angle and/ or length of the limbs.

4.Hierarchical visualization techniques (i.e. subspaces)

The subspaces are visualized in a hierarchical manner.