Neural Networks Algorithm - Data Mining

Introduction

Neural Networks are powerful machine learning algorithms inspired by the structure and function of the human brain. They excel at pattern recognition and prediction, making them invaluable for data mining tasks.

This page focuses on a fundamental layer: the Network Learning Algorithm.

Algorithm Overview

The Neural Networks Algorithm operates in a series of layers. Data flows through these layers, undergoing transformation and learning.

It consists of an input layer, hidden layers, and an output layer. The learning happens through adjustment of weights and biases during training.

Key Components

Activation functions are crucial. They introduce non-linearity, allowing the network to learn complex relationships.

Backpropagation is the algorithm used to adjust weights and biases for optimization.

Application in Data Mining

Neural Networks are increasingly used in data mining to:

• Pattern recognition
• Anomaly detection
• Classification
• Regression

Real-World Example (Simplified)

Imagine a dataset of customer purchasing history. A neural network can be trained to predict the next purchase a customer is likely to make.

Next Steps

You can explore this page further, and delve into the specifics of each section. Also, we have links to related resources!