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Multivariate Data Visualization Lab

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Lab Expt 3 Multivariate Data Visualization
Objective:
Demonstrate the following plots for different attributes from table 3.1
a) Parallel Coordinate plot for 3 attributes (Maxtemp , Weight & Height)
b) Multiple boxplots one the same graph for 4 attributes (Maxtemp, Weight Height years)
c) Matrix of scatter plots for quantitative attributes
d) Correlograms for Pearson correlation between all quantitative attributes
e) Heat map for all quantitative attribute with contacts data set.
f) Visualization of objects in contact data set wing Chernoff faces.
Description/Theory:
This assignment uses the table information given below.
Contact
Maxtemp
Weight
Height
Years
Gender
Compa
ny
Andrew
25
77
175
10
M
Good
Bernhard
31
110
195
12
M
Good
Carolina
15
70
172
2
F
Bad
Dennis
20
85
180
16
M
Good
Eve
10
65
168
0
F
Bad
Fred
12
75
173
6
M
Good
Gwyneth
16
75
180
3
F
Bad
Hayden
26
63
165
2
F
Bad
Irene
15
55
158
5
F
Bad
James
21
66
163
14
M
Good
Kevin
30
95
190
1
M
Bad
Lea
13
72
172
11
F
Good
Marcus
8
83
185
3
F
Bad
Nigel
12
115
192
15
M
Good

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a) Parallel coordinates plot:
It is one of the most popular , also known as a profile plot. Each object in a data set is
represented by a sequence of lines crossing several equally spaced parallel vertical axes,
one for each attribute. The lines for each object are concatenated, and each object is then
represented by consecutive straight lines, with up and down slopes. For a particular object,
these lines connect with the vertical axis at a position proportional to the value of the
attribute associated with the axis. The larger the value of the attribute, the higher the
position.
b) Multiple boxplots
A box and whisker plot—also called a box plot—displays the five-number summary of a
set of data. The five-number summary is the minimum, first quartile, median, third quartile,
and maximum. In a box plot, we draw a box from the first quartile to the third quartile. A
vertical line goes through the box at the median. The whiskers go from each quartile to the
minimum or maximum. A simple plot for univariate analysis – the box plot – can also be
used to present relevant information about the attributes in a multivariate data set. If the
number of attributes is not too large, a set of box plots, one for each attribute, can be used.
c) Matrix of scatter plots:
Scatter plots illustrate how the values of two attributes are correlated. They make it possible
to see how an attribute varies according to the variability of the other attribute. The plots
show how the predictive attributes correlate for different classes. Note that the same
information is presented above and below the main diagonal, since the correlation between
attributes x and y is the same as the correlation between y and x. As well as the position of
each object being set according to the values of two attributes, the plot presents, on the
vertical and horizontal axes, the values of each attribute.
d) Correlograms
The linear correlation matrix can be plotted in a correlogram, The darker the square
associated with two attributes, the more correlated they are. The correlogram represents
the correlations for all pairs of variables. The intensity of the color is proportional to the
correlation coefficient so the stronger the correlation (i.e., the closer to -1 or 1), the darker
the boxes.

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Multivariate Data Visualization Lab Expt 3 Objective: Demonstrate the following plots for different attributes from table 3.1 a) Parallel Coordinate plot for 3 attributes (Maxtemp , Weight & Height) b) Multiple boxplots one the same graph for 4 attributes (Maxtemp, Weight Height years) c) Matrix of scatter plots for quantitative attributes d) Correlograms for Pearson correlation between all quantitative attributes e) Heat map for all quantitative attribute with contacts data set. f) Visualization of objects in contact data set wing Chernoff faces. Description/Theory: This assignment uses the table information given below. Contact Maxtemp Weight Height Years Gender Compa ny Andrew 25 77 175 10 M Good Bernhard 31 110 195 12 M Good Carolina 15 70 172 2 F Bad Dennis 20 85 180 16 M Good Eve 10 65 168 0 F Bad Fred 12 75 173 6 M Good Gwyneth 16 75 180 3 F Bad Hayden 26 63 165 2 F Bad Irene 15 55 158 5 F Bad James 21 66 163 14 M Good Kevin 30 95 190 M Bad Lea 13 72 172 11 F Good Marcus 8 83 185 3 F Bad Nigel 12 115 192 15 M Good 1 a) Parallel coordinates plot: It is one of the most popular , al ...
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