The document discusses data visualization and pixel-oriented visualization techniques. It defines data visualization as using visual elements like charts and graphs to communicate data clearly. Pixel-oriented techniques represent each data point as a pixel, allowing large amounts of data to be visualized on a screen using color-coded pixels. The document provides examples of how pixel-oriented visualization can be used to visualize population data of countries and customer income data. It also discusses how heat maps, a type of pixel visualization, are commonly used in data mining to visualize correlation matrices and gene expression data.

1. Data Mining
Topic: Data Visualization
Name: Shahmeer
Roll No: 133
Semester: 6th Morning

2. Introduction
● Data visualization aims to communicate data clearly and
effectively through graphical representation.
● Data visualization is the graphical representation of
information and data using visual elements like charts,
graphs, and maps. It helps to see and understand trends,
outliers, and patterns in data.
● Data visualization has been used extensively in many
applications—for example, at work for reporting, managing
business operations, and tracking progress of tasks.

3. Continued
● 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.
● Nowadays, people also use data visualization to create fun
and interesting graphics.
● There are many basic concepts of Data visualization,
ranging from different types to terminologies that are used in
the subject of data mining.
● The limbo and concepts relating to the subject are necessary
to understand.

4. Continued
● Some commonly studied approaches include, pixel-oriented
techniques, geometric projection techniques, icon-based
techniques, and hierarchical and graph-based techniques.
● The visualization of complex data and relations is also
deeply emphasized in the subject.
● All of these approaches will be cleared in this presentation.

5. Data Visualization Advantages
Faster and easier processing: Visual content is processed much
faster and easier than text by the human brain. Data visualization
helps to analyze data quickly and accurately.
Simple data sharing: Data visualization makes sharing data
simple and easy because it ensures that everyone is on the same
page when they’re viewing the visualization. It also helps to tell a
story with data and engage the audience.
Better analysis: Data visualization helps to spot trends, patterns,
outliers, and relationships in data that might otherwise be missed

6. Continued
Quicker decisions: Data visualization helps to make informed
decisions based on data insights. It also helps to test
hypotheses and scenarios and evaluate outcomes.
Exploration and collaboration: Data visualization helps to
explore data in an interactive and creative way. It also fosters
collaboration and communication among different stakeholders
and teams.

7. Data Visualization Disadvantages
● Biased or inaccurate information: Data visualization can be
misleading or distorted if the data is not accurate, complete,
or representative. It can also be influenced by the choice of
colors, scales, shapes, and labels.
● Correlation vs causation: Data visualization can show
correlations between variables, but not necessarily
causation. It can also be affected by confounding factors or
spurious relationships that are not relevant or meaningful.

8. Continued
● Complexity and clutter: Data visualization can be complex
and cluttered if it tries to show too much information or too
many dimensions. It can also be confusing or overwhelming
if it is not clear, consistent, or intuitive.

9. Data Visualization Examples - Bar Chart
● A bar chart uses horizontal or vertical bars to show the values of
different categories or groups. It is useful for comparing data across
categories or showing changes over time. For example, you can use a
bar chart to show the sales of different products in each quarter.

10. Line Chart
● A line chart uses points connected by lines to show the values of a
variable over time or along a scale. It is useful for showing trends,
patterns, or fluctuations in data. For example, you can use a line chart
to show the stock price of a company over a year.

11. Pie Chart
● A pie chart uses a circular shape divided into slices to show the
proportions of different categories or groups. It is useful for showing the
relative sizes of parts of a whole. For example, you can use a pie chart to
show the market share of different brands in a sector.

12. Pixel-Oriented Visualization Techniques
● Pixel-oriented data visualization technique is a way of
showing a lot of information on a screen by using small dots
of different colors.
● Each dot represents one piece of information and each color
means something different.
● For example, you can use this technique to show how many
people live in different countries by using different colors for
different population ranges.

13. Continued
● This technique can help users see patterns and differences
in the information more easily. Since each pixel represents a
separate set of data, it makes it easier for a lot of data to be
shown on the screen at any given moment.
● This technique uses small dots, called pixels, to show the
information. Each pixel has a color that means something.

14. Continued
● For example, pixels can be put in a line or a circle or a spiral. By
using this technique, a lot of information can be seen at once and
compared easily; it is possible because of the sheer amount of
pixels that can be presented on a screen at once.
● Imagine you have a lot of information about different things, like
cars, animals, or countries. You want to see this information on a
screen, but there is too much to fit in a normal way. So you use a
technique called pixel-oriented data visualization

15. Continued
● Data can be put in a spiral or a circle. Moreover, different
windows can also be used to represent data.
● For example, you can use one window to show the speed of
the cars, another window to show the price of the cars, and
another window to show the size of the cars.
● For another example, you can compare the population, the
area, and the GDP of different countries by using three
windows with different colors for each attribute.

16. Diagram example
● We can sort all customers in income-ascending order, and
use this order to lay out the customer data in the four
visualization windows, as shown in Figure below. The pixel
colors are chosen so that the smaller the value, the lighter
the shading.

17. How it is used in data mining?
One example of pixel-oriented visualization is the heat map,
which is commonly used in data mining and machine learning.
Heat maps use color to represent the values of a matrix or table,
with darker colors indicating higher values and lighter colors
indicating lower values. Heat maps are commonly used to
visualize correlation matrices, gene expression data, and other
types of data.