Data Visualization Data Visualization Data Visualization

Chapter 3 – Data Visualization
© Galit Shmueli and Peter Bruce 2010
Shmueli, Patel & Bruce
1

Graphs for Data Exploration
Basic Plots
Line Graphs
Bar Charts
Scatterplots
Distribution Plots
Boxplots
Histograms
2 © Galit Shmueli and Peter Bruce 2010

Line Graph for Time Series

Bar Chart for Categorical
Variable
95% of tracts do not
border Charles River
Excel can confuse:
y-axis is actually “% of
records that have a
value for CATMEDV”
(i.e., “% of all records”)

Scatterplot
Displays relationship between
two numerical variables

Distribution Plots
 Display “how many” of each value occur in a data
set
 Or, for continuous data or data with many
possible values, “how many” values are in each of
a series of ranges or “bins”

Histograms
Histogram shows the
distribution of the
outcome variable
(median house value)
Boston Housing
example:

Boxplots
Boston Housing
Example: Display
distribution of outcome
variable (MEDV) for
neighborhoods on
Charles river (1) and not
on Charles river (0)
Side-by-side boxplots are useful for comparing subgroups

Box Plot
 Top outliers defined
as those above
Q3+1.5(Q3-Q1).
 “max” = maximum of
non-outliers
 Analogous definitions
for bottom outliers and
for “min”
 Details may differ
across software
Median
Quartile 1
“max”
“min”
outliers
mean
Quartile 3

Heat Maps
Color conveys information
In data mining, used to visualize
Correlations
Missing Data

Heatmap to highlight correlations
(Boston Housing)
CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B LSTAT MEDV
CRIM 1.00
ZN -0.20 1.00
INDUS 0.41 -0.53 1.00
CHAS -0.06 -0.04 0.06 1.00
NOX 0.42 -0.52 0.76 0.09 1.00
RM -0.22 0.31 -0.39 0.09 -0.30 1.00
AGE 0.35 -0.57 0.64 0.09 0.73 -0.24 1.00
DIS -0.38 0.66 -0.71 -0.10 -0.77 0.21 -0.75 1.00
RAD 0.63 -0.31 0.60 -0.01 0.61 -0.21 0.46 -0.49 1.00
TAX 0.58 -0.31 0.72 -0.04 0.67 -0.29 0.51 -0.53 0.91 1.00
PTRATIO 0.29 -0.39 0.38 -0.12 0.19 -0.36 0.26 -0.23 0.46 0.46 1.00
B -0.39 0.18 -0.36 0.05 -0.38 0.13 -0.27 0.29 -0.44 -0.44 -0.18 1.00
LSTAT 0.46 -0.41 0.60 -0.05 0.59 -0.61 0.60 -0.50 0.49 0.54 0.37 -0.37 1.00
MEDV -0.39 0.36 -0.48 0.18 -0.43 0.70 -0.38 0.25 -0.38 -0.47 -0.51 0.33 -0.74 1.00
In Excel
(using
conditional
formatting)
In Spotfire

Multidimensional Visualization

Scatterplot with color added
Boston Housing
NOX vs. LSTAT
Red = low median
value
Blue = high median
value

Matrix Plot
0
0
1.8
1.8
3.6
3.6
5.4
5.4
7.2
7.2
9
9
0
0
0.2
0.2
0.4
0.4
0.6
0.6
0.8
0.8
1
1
0
0
0.6
0.6
1.2
1.2
1.8
1.8
2.4
2.4
3
3
CRIM
101
ZN
102
INDUS
101
Matrix Plot
Shows scatterplots
for variable pairs
Example:
scatterplots for 3
Boston Housing
variables

Rescaling to log scale (on right)
“uncrowds” the data

Aggregation

Amtrak Ridership – Monthly Data

Aggregation – Monthly Average

Aggregation – Yearly Average

Scatter Plot with Labels (Utilities)

Scaling: Smaller markers, jittering, color
contrast (Universal Bank; red = accept loan)

Jittering
 Moving markers by a small random amount
 Uncrowds the data by allowing more markers to
be seen

Without jittering (for comparison)

Parallel Coordinate Plot (Boston Housing)
Filter
Settings
-
CAT.
MEDV:
(1)
CATMEDV =1
CATMEDV =0

Linked plots
(same record is highlighted in each plot)

Network Graph – eBay Auctions
(sellers on left, buyers on right)
Circle size = # of
transactions for the
node
Line width =# of
auctions for the buyer-
seller pair
Arrows point from
buyer to seller

Treemap – eBay Auctions
(Hierarchical eBay data:
Category> sub-category> Brand)
Rectangle size
= average
closing price
(=item value)
Color = %
sellers with
negative
feedback
(darker=more)

Map Chart
(Comparing countries’ well-being with GDP)
Darker = higher value

Summary of Major Visualizations &
Operations, According to Data Mining Goal

Data Visualization Data Visualization Data Visualization

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