The document discusses data visualization techniques for visual data mining. It defines key terms like visual, visualization, and visual data mining. Visual data mining uses visualization techniques to discover useful knowledge from large datasets. Benefits include faster understanding of problems, insights, and trends in data. Different graph types like bar charts, histograms, pie charts and scatter plots are suitable for different purposes like comparing values or showing relationships. Effective visualization requires arranging data clearly, identifying important variables, choosing the right graph, keeping it simple, and understanding the audience.

1. ByThilinaWanshathilaka

2.  Visual :
something such as a picture, photograph,
or piece of film used to explain something
(https://dictionary.cambridge.org)
 Visualization:
the act or an example of creating aVisual. to represent
something with massive amount of data and higher
complexity
((https://dictionary.cambridge.org) )
 Visual data mining: is the process of discovering implicit
but useful knowledge from large data sets using
visualization techniques.

3.  Understand and analyze the problem domain
faster .
 Efficient data assimilation .
 Fast access to business insight .
 Fast identification of trends .
 Direct interaction with data.
 Offer different perspective for same data.
 Easy to conduct business analytics
 Increase efficiencyAI based expert systems.

4. Tables are good when Graphs are good when
User requires to look up for
specific values.
User needs to absorb and
process data fast
When user required precise
data
When user wants to see the
relationships between values
User needs to precisely
compare related values
When user needs to see
patterns and trend
User requires data with
different set of measurements
Has large dataset
The message is contained in
the shape of the values

5.  Bar chart
 Histogram
 Pie chart
 Line chart
 Area chart
 Scatter plot
 Bubble chart

6.  Presents categorical variables.
 Height of the bar represent value.
 Bars can be stacked or put closer together or
used 3D rendering .
 Can be horizontal or vertical
 Very simple and easy to understand
 Can be used as display of value and
parentages as well

7. Analysis of locations and Membership durations in 2004
0
100
200
300
400
500
600
700
ChurchWall
CollegiateTableTennis
ForgersTableTennis
ChurchStreetFitnessSuite
PsalterGym
CityTableTennis
PsalterFitnessSuite
ForgersFitness
CollegiateFitnessSuite
CollegiateGym
CitySportsHall
CityPool
CityGym
CityFitness
CollegiateSportsHall
PsalterSplash
4 6 8 10 12 15 20 30
Short Medium Long

8. Total attendance of members by year and membership type
MemberTypeDesc 2002 2003 2004
Senior Citizen 267 335 347
Staff 348 374 434
Platinum 395 401 420
Casual 468 498 603
Gold 610 628 569
Bronze 576 664 670
Silver 828 844 858
Grand Total 3,492 3,744 3,901
0
100
200
300
400
500
600
700
800
900
1,000
2002 2003 2004
TotalAttendance

9. Attendance based Membership types in 2004.
43
51
50
71
79
83
120
359
485
491
641
712
801
969
235
294
329
408
474
491
680
121
137
117
193
242
227
312
191
189
229
256
300
308
449
Senior Citizen
Staff
Platinum
Casual
Gold
Bronze
Silver
Church Street
City Campus
Collegiate Crescent
Forgers
Psalter Lane

10. Pie Chart for Census Data
Source : 2013 Pearson Education, Inc. publishing as Prentice Hall

11.  Pie charts summarize a set of
categorical/nominal data
 Emphasize percentages related to all
variables in the domain
 Not good at display values or when you have
too many variables
 Easily misinterpret and use with care

12.  Total attendance of gym members by months for the period 2002 to 2004 .
0
50
100
150
200
250
300
350
400
450
500
1 2 3 4 5 6 7 8 9 10 11 12
2002
2003
2004

13.  Very simple and fundamental representation
of data
 Very good at showing trends
 Good at showing quantitative data
 Can use to display multiple values (multiple
lines )
 Can easily combine with other graphs such as
bar charts ,histogram ,scatter plots


14.  Variant of line chart.
 Good at showing trends
 Good at showing quantitative data
 Good for displaying relationships
 Sudden dips and climax could distort
presentation

15. Source : 2013 Pearson Education, Inc. publishing as Prentice Hall

16.  In basic form it will use to represent
relationship between two variables
 Effective if there is a relationship between
two variable
 Very effective when representing continues
data.
 Very effective tool to identify cluster patterns
 Can use 3D scatter plots ,bubble charts to
represent multiple variables .

17. Source : 2013 Pearson Education, Inc. publishing as Prentice Hall

18.  Other chart
 Maps
 Animations
 Data models

20. Source : http://www.cs.put.poznan.pl/jstefanowski/sed/DM14-visualisation.pdf

21. Source : 2013 Pearson Education, Inc. publishing as Prentice Hall

22. Source: https://en.wikipedia.org/wiki/Pie_chart#/media/File:Badpie.png

23. 0
50
100
150
200
250
300
350
400
450
500
1 2 3 4 5 6 7 8 9 10 11 12
2002
2003
2004

24.  Arrange data in a clear and presentable
manner
 Clearly name the axis and values.
 Color code different categories.
 Organize data in strategic format (if you monitor
sales values make sure to sort them)
 Avoid cluttering .

25.  Understand what is vital information should
captured by your visualization.
 Identify what are the variables that impotent to
you or your organization
 Understand the association between those
variables
 Identify the objective of the data visualization.
 Don’t present meaningless data because you have
them
 Avoid data glut

26.  Identify the right chart for represent data
 Know what you want to present to your audience.
 Know who are your audience.
 Make sure that the chart will not alter ,
misinterpret or giving wrong conclusion about
data.
 Please read the article data visualization 101 by
Jami Oetting [online ] last accessed 10-10-2018
https://blog.hubspot.com/marketing/types-of-
graphs-for-data-visualization.

27.  Keep it simple
 The main objective of data visualization is keep
data simple easy to understand and analyze.
 Use simple language.
 Don’t use adverbs or expressive language.
 Label your chart clearly and simple, make it easy
to read and view.

28.  Please watch theYouTube documentary Hans
Rosling's 200 Countries, 200Years, 4 Minutes
-The Joy of Stats - BBC 4
https://www.youtube.com/watch?v=jbkSRLY
Sojo.
 Please watch theYouTube video Cole
Nussbaumer Knaflic: "Storytelling with Data"
https://www.youtube.com/watch?v=8EMW7i
o4rSI&feature=youtu.be&t=28.

29.  Pearson Education, (2013)DataVisualization and Exploring
Data. Prentice Hall.[Online]Last accessed
http://cs.furman.edu/~pbatchelor/csc105/MyPPT/Visualizing
%20Data.pptx .
 J. Stefanowski, (2013) DataVisualization or Graphical Data
Presentation. .[Online]Last accessed
http://www.cs.put.poznan.pl/jstefanowski/sed/DM14-
visualisation.pdf