Table of Contents
Workload Characterization Techniques
Overview
Terminology
Components and Parameter Selection
Workload Characterization Techniques
Averaging
Case Study: Program Usage in Educational Environments
Characteristics of an Average Editing Session
Single Parameter Histograms
Multi-parameter Histograms
Principal Component Analysis
Finding Principal Factors
Principal Component Example
Principal Component Example
Markov Models
Transition Probability
Clustering
Clustering Steps
1. Sampling
2. Parameter Selection
3. Transformation
4. Outliers
5. Data Scaling
Distance Metric
Clustering Techniques
Minimum Spanning Tree-Clustering Method
Minimum Spanning Tree Example
Dendogram
Nearest Centroid Method
Cluster Interpretation
Problems with Clustering
Summary
Exercise 6.1 |
Author:
Raj Jain
Home Page:
http://www.cse.wustl.edu/~jain/
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