Visualization for Classification and Clustering Techniques

1
Visualization for Classification and Clustering
Techniques

• Marc René
• CSE 8331
• Data Mining - Project 1

2
Overview

• Importance of Data Visualization in the KDD
  Process
• Understanding and Trust
• Visualization techniques
• Classification
• Clustering
• Future Directions

3
KDD Process

• Selection
• Obtain data from all of sources
• Preprocessing
• After selecting the data, clean it to make sure
  it is consistent
• Transformation
• After preprocessing the data, analyze the
  format/amount of data
• Data Mining
• Once the data is in a useable format/content,
  apply various algorithms based upon the results
  trying to be achieved
• Interpretation/Evaluation
• Finally, present the results of the data mining
  step to the user, so that the results can be used
  to solve the business need at hand

4
Importance of Data Visualization

• The final step in the KDD process
• Highly dependent on the Data Visualization
  technique
• Bad/inappropriate technique may result in
  misunderstanding
• Misunderstanding may cause an incorrect (or no)
  decision

It is important to consider that the KDD process
is useless if the results are not understandable
5
Current Issues w/Data Visualization

• The literature suggests a significant reliance on
  expert users
• General lack of data visualization support in
  many data mining tools Goebel99
• These are significant problems if KDD/DM/Data
  Visualization will expand at the rates suggested
• Data visualization tool market 2.2 billion by
  2007 Nuttall03

6
Suggested Direction

• Need to determine techniques that balance
  simplicity with completeness
• If this can be done for non-expert users
• Simplicity Completeness ? Understanding
• Understanding ? Trust
• Trust ? more use of KDD/DM
• Result will be
• Better business value
• Higher ROI

7
Common Visualization Techniques

• Visualization techniques dependent upon
• The type of data mining technique chosen
• The underlying structure and attributes of the
  data
• Classification Clustering
• Decision Trees - Scatter Plots
• Scatter Plots - Dendrograms
• Axis-Parallel Decision Trees - Smoothed Data
  Histograms
• Circle Segments - Self-Organizing Maps
• Decision Tables - Proximity Matrixes

8
Classification
9
Decision Tree

• Information limited to
• Attributes
• Splitting values
• Terminal node class assignments

10
Decision Tree with Histograms

• Data mining rarely classify 100 of the data
  correctly
• Include the success of properly classifying the
  data - histogram added for each terminal node
• Percentage of data that was classified
  correctly/incorrectly
• Assists users in determining if the
  classification is good enough

11
Decision Tree - Different Format

• Vertical representation - allows for easy user
  interaction
• Combines the split points and classification
  accuracy - compactly
• Key difference - colors are matched with a
  specific classification

12
Scatter Plot with Regression Line

• Excellent way to view 2-dimensional data
• Familiar to anyone who has taken high-school
  algebra
• Regression lines provide descriptive techniques
  for classification

13
Axis-Parallel Decision Tree

• Combination Scatter Plot and Decision Tree
• Areas divided in parallel regions on the axis
• Well suited for classification problems with two
  attribute values
• High visibility into the impact of outliers

14
Circle Segments

• Multi-dimension data
• Maps dataset with n dimensions onto a circle
  divided by n segments
• Each segment is a different attribute
• Each pixel inside a segment is a single value of
  the attribute
• Values of each attribute are then sorted
  (independently) and assigned a different colors
  based upon its class

15
Decision Table

• Interactive technique
• Maps attribute data to a 2D hierarchical matrix
• Levels can be drilled down - another set of
  attributes
• Height of a cell conveys the number of data
  entities
• Cells color coded
• Neutral color ? no data in that intersection
  point
• Color coded by class (percentage)

16
Decision Table
17
Clustering
18
Scatter Plot

• Extensions include, displaying points in
• Various sizes and colors to indicate additional
  attributes
• Shading of points to introduce a third dimension
• Using different brightness levels of the same
  color to represent continuous values for the same
  attribute
• Using various points or classification
  identifiers (i.e., numbers, symbols)
• Using various glyphs to display additional
  attributes

19
Scatter Plot

• Map decision trees on top of scatter plots to
  describe clusters

20
Scatter Plot with Regression Lines
21
Scatter Plot w/Min Spanning Tree
22
Dendrogram

• Intuitive representation - hierarchical
  decomposition of data into sets of nested
  clusters.
• From an agglomerative perspective
• Each leaf - a single data entity
• Each internal node - the union of all data
  entities in its sub-tree
• The root - the entire dataset
• The height of any internal node - the similarity
  between its children.

23
Dendrogram with Exemplars

• The most typical member of each cluster
  Wishart99
• Underlined labels of the leafs
• Done in combination with shading to identify the
  clustering level

24
Smoothed Data Histogram

• Represents data on a display map
• Similar data items are located close to each
  other
• More defined the clusters lighter colors

25
Smoothed Data Histogram - Detail
26
Self-Organizing Map Grid

• Source of Smoothed Data Histogram
• Numbers indicate most common cluster

27
Proximity Matrix

• Graphically display the relationship between data
  elements
• Usually symmetric, but can be sorted by the
  strength of relationships

28
Proximity Matrix and Dendrogram
29
Summary

• Data visualization techniques are extremely
  important for understanding the KDD process
• A balance of simplicity and completeness is
  important
• The techniques discussed allow average users to
  understand the results of the KDD process
• Understanding ? KDD results to be
  interpreted/trusted by non-expert users ?
  extending the business value
• If data visualization techniques do not establish
  a high level of trust in the KDD process, the
  process will fail

30
Future Direction

• Significant effort will be spent on improving
  data visualization techniques in the next few
  years
• KDD process and data mining are becoming more
  widespread
• Business will expect tools to become more
  user-friendly and support the varied level of
  skills
• Trends are moving to a more interactive mode
• Static reporting techniques (i.e., standard
  decision trees, standard circle segments) are
  being replaced
• Interactive techniques (i.e., smoothed data
  histograms, interactive circle segments and
  decision tables)
• Very interactive data models ? virtual reality
  are also being considered/proposed

31
ReferencesPart 1

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32
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