Perception-Based Classification(PBC) System

Salvador Ledezma

sledezma@uci.edu

April 25, 2002

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

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

Classification

Major task of Data Mining Assign object to one of a set of given classes

based on object attributes

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

Classification Example

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

Visual Data Mining

Tackle data mining tasks by enabling human involvement Incorporating perceptivity of humans

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

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

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

Represents 50 stocks. 1 circle represents the prices of different stocks at the same time

Light = high stock price

Dark = low stock price

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

DNA Training Data

Attribute 85 and attribute 90 visually are good candidates for splitting tree

Algorithm picks 90 as the optimal split

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

Additional Functionality

Propose split Look-ahead

For a hypothetical split

Expand tree Automatic expanding and construction

PBC demo