1. Perception-Based Classification (PBC) System Salvador Ledezma sledezma@uci.edu April 25, 2002

2. Introduction  Concepts  Demo of PBC  References: “Towards and Effective Cooperation of the User and Computer for Classification” “Visual Data Mining with Pixel-oriented Visualization Techniques” “Visual Classification: An Interactive Approach to Decision Tree Construction” Mihael Ankerst, author or coauthor

3. Data Mining  Exploration and Analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns and rules  Part of Knowledge Discovery in Databases (KDD) process

4. Classification  Major task of Data Mining  Assign object to one of a set of given classes based on object attributes

5. Classification Algorithms  Decision Tree Classifier  Training set – set of objects whose attributes and class is already known  Using training set, tree classifier determines a classification function represented by a decision tree Model for class attribute as a function of the values of other attributes  Test set – validates the classification function

6. Classification Example

8. Classification (cont)  Usually algorithms are black boxes with no user interaction or intervention  Reasons for user involvement in decision tree construction: Use human pattern recognition capabilities User will have better understanding of tree User provides domain knowledge

9. Visual Data Mining  Tackle data mining tasks by enabling human involvement Incorporating perceptivity of humans

10. Visual Classification  Construction of decision trees is decomposed into substeps  Enables human involvement  Example: PBC  Data visualization based on 2 concepts Each attribute of training data is visualized in a separate part of screen Different class labels of training objects are represented by different colors

11. Pixel-Oriented Visualization Techniques  Represent each attribute value as a single colored pixel  Map the range of possible attribute values to a fixed color map  Maximizes the amount of information represented at one time without any overlap

12. Circle Segments Technique  Data is a circle divided into segments  Each segment represents an attribute  Attribute values are mapped by a single colored pixel and arrangement starts in the center and proceeds outward  Example

13. Represents 50 stocks. 1 circle represents the prices of different stocks at the same time Light = high stock price Dark = low stock price

14. Bar Visualization  For each attribute Attribute values are sorted into attribute lists Classes are defined by colors  Within a bar, sorted attribute values are mapped to pixels, line by line  Each attribute is placed in a different bar

15. DNA Training Data Attribute 85 and attribute 90 visually are good candidates for splitting tree Algorithm picks 90 as the optimal split

16. PBC  Uses pixel-oriented visualization  Visualizes training data in order to support interactive decision tree construction  Examples of use Automatic Automatic-manual (top 2 levels) Manual-automatic Manual Actual use lies somewhere in between this spectrum

18. Additional Functionality  Propose split  Look-ahead For a hypothetical split  Expand tree Automatic expanding and construction

19. PBC demo