perception-based classification (pbc) system salvador ledezma [email protected] april 25, 2002
TRANSCRIPT
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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
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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
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Classification
Major task of Data Mining Assign object to one of a set of given classes
based on object attributes
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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
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Classification Example
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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
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Visual Data Mining
Tackle data mining tasks by enabling human involvement Incorporating perceptivity of humans
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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
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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
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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
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Represents 50 stocks. 1 circle represents the prices of different stocks at the same time
Light = high stock price
Dark = low stock price
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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
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DNA Training Data
Attribute 85 and attribute 90 visually are good candidates for splitting tree
Algorithm picks 90 as the optimal split
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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
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Additional Functionality
Propose split Look-ahead
For a hypothetical split
Expand tree Automatic expanding and construction
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PBC demo