Data mining: unsupervised learning and clustering · Introduction

Introduction

What this guide covers

A rigorous and exhaustive theoretical analysis of the taxonomy of unsupervised learning. Detailed mathematical exploration of Hilbert spaces, topology of neighborhood networks, convergence of the Expectation-Maximization algorithm and spectral decomposition of Laplacian graphs.

Overview

Topics covered

Introduction
Statistical Clustering Models
Theory of Distances and Metric Spaces
1.1 Metric Families in Continuous Characteristic Spaces
Minkowski distance
Mahalanobis Distance

Related concepts

Main connections

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

d_p(\mathbf{x}, \mathbf{y}) = \left( \sum_{i=1}^d |x_i - y_i|^p \right)^{1/p}, \quad p \ge 1

View LaTeX source

d_p(\mathbf{x}, \mathbf{y}) = \left( \sum_{i=1}^d |x_i - y_i|^p \right)^{1/p}, \quad p \ge 1