程序代做CS代考 Excel Semester 2 2021 – cscodehelp代写
Semester 2 2021
Lecture 2: Visualisation – Part IV
Basic Visualisation
✓Line plots ✓Boxplots ✓Histograms ✓Bar charts ✓Scatter plots • Heat maps
• Parallel Coordinate plots
Which Iris is which?
Which one is Setosa and which one is Virginica?
Heat maps
• Plot the data matrix
• Individual values contained in a matrix are represented as colours • This can be useful when objects are sorted according to class/type
• Typically, features are normalised to prevent one attribute from dominating the plot
Drawing a heat map
-1.5 0.9 -1.9 -1.2
Heat map – (normalised) Iris data
[Columns have been standardized to have a mean of zero and standard deviation of 1]
How common is your birthday?
https://www.abc.net.au/news/2017-12-13/australias-most-and-least-popular-birthdays-revealed/9241978
Parallel Coordinates
• A widely used visualisation technique for exploring multi-dimensional data sets
• Use a set of parallel axes (coordinate axes)
• The values of each data object are plotted as a point on each
corresponding coordinate axis and the points are connected by a line. • Thus, each data object is represented as a line
Parallel coordinates – Iris data
Note: normalised measurements by subtracting mean and dividing by the standard deviation https://www.data-to-viz.com/graph/parallel.html
Patterns in parallel coordinates
• Reveal a distinct class of object group • Show data characteristics such as
• different data distributions
• Associations of feature pairs.
Patterns – cont.
• Highlight specific patterns of association on different features
http://joules.de/files/heinrich_parallel_2015.pdf
Patterns – cont.
• Highlight specific patterns of association on different features
http://joules.de/files/heinrich_parallel_2015.pdf
Axes scaling with parallel coordinates
Scaling of Axes
• Inconsistent scaling can lead to mis-interpretation
https://aedeegee.github.io/cgf12.pdf
Axes scaling – cont.
• Axes scaling affects the visualization
• May choose to scale all features via a pre-processing step
https://www.data-to-viz.com/graph/parallel.html
https://aedeegee.github.io/cgf12.pdf
Axes ordering in parallel coordinates
Ordering of axes
• Influences the relationships that can be seen. Correlations between pairs of features may only be visible in certain orderings
• Can decrease the clutter
• Can reveal distinct class more clearly
Parallel coordinates – ordering of axes
https://www.data-to-viz.com/graph/parallel.html
Very high dimensional data?
• Parallel coordinates leads to clutter and over-plotting with very large dataset and very high dimensions
• Not enough space to draw all lines
• Difficult to trace a line for a data object
• Only look at an important subset of attributes • Domain experts
• Feature selection techniques
• Dimensionality reduction techniques: covered later in the subject
Elements of a good visualisation
• Meaningful title
• Appropriate scales, annotation
• Suitability to the dataset and the context of the data question
• Can be interpreted on its own.
• Caption can be used to explain the context, the dataset, and a brief
interpretation of plot, where appropriate.
• Has no redundant, information unimportant to the plot.
Summary
• Visualisation tools allow a quick summary of the data • Easy to glean the important features
• Can be a visual tool to help analysis
• Assist in getting to know your data
• Excellent communication tool
• Given your data:
• What are the best ways to visualise the information?
• Different aspects of the data may lend themselves better to different visualisations
