Data Mining vs Web Scraping: Key Differences, Examples, and Proxy Use Cases

For example, a retailer could scrape product prices from several online stores, like Amazon, and then use data mining to identify discount patterns, compare brands, or forecast price changes. In that workflow, scraping creates the dataset and mining makes useful data out of it.

This guide compares data mining vs web scraping, explains how they work together, and shows where proxies improve data collection.

IBM defines data mining as the use of machine learning and statistical analysis to uncover patterns and useful information in large datasets.

The source data may come from internal databases, customer records, transaction histories, sensors, public datasets, or websites. Before analysis begins, teams usually clean the data, remove duplicate records, correct formatting problems, and decide which variables are relevant.