Data mining
Data mining is the computational or systematic discovery of patterns, relationships, or other useful information in data.
Inside AI
Data mining is the computational process of discovering patterns, relationships, and useful insights in large datasets.
- How it is used
- Extracts actionable patterns from large datasets for retail decisions and fraud prevention.
- How deep it goes
- Defines major tasks and outlines an iterative project process, without developing algorithm theory.
- Examples
- Uses grocery stocking and promotions, receipt-based market basket analysis, and credit-card fraud decision trees.
- What you can do after
- Evaluate/understand it by matching a data-mining task to a business question and recognizing the preparation and testing involved.
Introduction to Generative AI, Second Edition
Data mining is the computational analysis of large collections of data to discover patterns, information, or relationships.
- How it is used
- Frames data mining as a copyright question surrounding image datasets used by generative AI.
- How deep it goes
- States a German legal condition and describes a removal dispute, without explaining mining algorithms or resolving the dispute’s legality.
- Examples
- Uses LAION-5B, a photographer’s removal request, and Stability AI’s opt-out announcement.
- What you can do after
- Evaluate/understand why mining creative work raises questions about access, removal, and artists’ control.
Read how Introduction to Generative AI, Second Edition explains data mining →
Grokking Statistics
Data mining is the process of discovering patterns or relationships by analyzing data, often before formulating a specific hypothesis.
- How it is used
- Uses exploratory analysis to discover hypotheses or patterns before committing to a specific question.
- How deep it goes
- Explains the modeling sequence and cautions about testing, context, bias, representativeness, and p-hacking.
- Examples
- Uses sports-drink sales, family photographs, golf performance, scorecards, and favorable p-values as concrete illustrations.
- What you can do after
- Evaluate/understand exploratory findings while checking generalization, context, representativeness, bias, and selective analysis.
WordPress Power Toolkit: Harness AI to build next-level websites
Deep data mining is the computational analysis of large or complex datasets to discover patterns, relationships, anomalies, and predictive signals.
- How it is used
- Supports search-engine optimization and audience-building by turning large-scale behavior data into patterns, predictions, and website insights.
- How deep it goes
- Defines large-scale pattern discovery and illustrates prediction, but stops short of explaining algorithms or implementation details.
- Examples
- Uses social-media trend detection, Amazon purchase matching, television-and-antenna recommendations, and an intelligent-drone analogy.
- What you can do after
- Evaluate how AI-derived patterns can explain rankings, predict behavior, and guide improvements to a website or recommendation experience.
Read how WordPress Power Toolkit: Harness AI to build next-level websites explains data mining →
Related concepts
- scientific methodcontrasts with · The scientific method is a systematic process of investigating questions by forming hypotheses, gathering and evaluating evidence, and drawing or revising conclusions.
- association rule miningincludes · Association rule mining is a data-mining technique that discovers relationships or implications among items, events, or categorical values based on their co-occurrence.
- datasetuses · A dataset is an organized collection of related data items, records, or observations treated as a unit for storage, processing, analysis, or computation.
- language modelproduces · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.