unsupervised learning编辑历史

# Unsupervised Learning

**Overview:**  
Unsupervised learning is a category of machine learning techniques that derive insights from unlabeled data without predefined output labels. Unlike supervised learning, which relies on input-output pairs, unsupervised learning algorithms identify hidden patterns, structures, or groupings within the data autonomously. Common tasks include clustering (grouping similar data points), dimensionality reduction (compressing data while preserving key features), and association rule learning (discovering relationships between variables). It is widely used in customer segmentation, anomaly detection, data visualization, and exploratory data analysis.

## 1 Introduction

### 1.1 Definition and Motivation

Unsupervised learning refers to a class of algorithms that operate on datasets containing only input features and no corresponding target variables. The central motivation is to discover inherent structures in the data—such as clusters, latent representations, or frequent patterns—without human-provided guidance. This is especially valuable when labeling is expensive, impractical, or impossible, or when the goal is exploratory analysis rather than prediction.

### 1.2 Comparison with Supervised Learning

In supervised learning, the model is trained on labeled pairs (input, output) to learn a mapping function. Unsupervised learning, by contrast, has no such feedback. Supervised methods aim to minimize prediction error on known outputs, while unsupervised methods optimize criteria such as cluster compactness, reconstruction error, or statistical independence. Unsupervised learning is often used as a precursor to supervised tasks, for example in feature learning or data preprocessing.

## 2 Key Tasks

### 2.1 Clustering

Clustering partitions data into groups (clusters) such that objects within the same cluster are more similar to each other than to those in other clusters, according to a defined distance or similarity measure.

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