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Contents
Table of Contents
1 Introduction
2 Loading, filtering, and adjusting data
2.1 Loading Data
2.2 Filtering Data
2.3 Adjusting Data
2.3.1 Log transformation
2.3.2 Mean/Median Centering
2.3.3 Normalization
3 Distance/Similarity measures
3.1 Distance measures based on the Pearson correlation
3.2 Non-parametric distance measures
3.3 Distance measures related to the Euclidean distance
3.3.1 Euclidean distance
3.3.2 City-block distance
3.4 Missing values
3.5 Calculating the distance matrix
4 Clustering techniques
4.1 Hierarchical Clustering
4.1.1 Centroid Linkage Clustering
4.1.2 Single Linkage Clustering
4.1.3 Complete Linkage Clustering
4.1.4 Average Linkage Clustering
4.1.5 Weighting
4.1.6 Ordering of Output File
4.1.7 Output Files
4.2 The
k
-means Clustering Algorithm
4.3 Self-Organizing Maps
4.4 Principal Component Analysis
5 Running Cluster 3.0 as a command line program
6 TreeView
7 Code Development Information
8 Bibliography