Chapter 3 – Data Visualization © Galit Shmueli and Peter Bruce 2010 Data Mining for Business Intelligence Shmueli, Patel & Bruce.

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Presentation transcript:

Chapter 3 – Data Visualization © Galit Shmueli and Peter Bruce 2010 Data Mining for Business Intelligence Shmueli, Patel & Bruce

Graphs for Data Exploration Basic Plots Line Graphs Bar Charts Scatterplots Distribution Plots Boxplots Histograms

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) 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 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