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Data mining and business knowledge

Short answer

Data mining automatically discovers hidden patterns in large data. The process: define business goal, select and preprocess data from warehouse, transform, apply mining techniques (classification, clustering), evaluate patterns, and deploy knowledge for decisions.

What do you mean by Data Mining? How is it related to Data Warehouse? Discuss the process of generating business knowledge using data mining techniques.

Data mining

Data mining is the process of automatically discovering hidden, valid and useful patterns, trends and relationships from large volumes of data using statistical, machine learning and database methods.

Relation with a data warehouse

A data warehouse stores integrated, cleaned, historical data from many sources. Data mining works best on this data because it is consistent, large and covers time. The warehouse is the data source; mining is the analysis layer on top of it (along with OLAP). Findings may be stored back to the warehouse.

Process of generating business knowledge (KDD steps)

 Business goal -> Data selection -> Cleaning
   -> Transformation -> Mining -> Evaluation -> Deployment
  1. Define the business problem - e.g. reduce customer churn.
  2. Select data from the warehouse relevant to the problem.
  3. Pre-process and clean - remove noise, handle missing values, remove duplicates.
  4. Transform - integrate, aggregate, create attributes, reduce dimensions.
  5. Apply mining techniques - classification, clustering, association rules, regression, anomaly detection.
  6. Evaluate patterns - check accuracy, validity and usefulness with domain experts.
  7. Present and deploy knowledge - reports and visualisation; use in decision making, such as targeted offers.
  8. Monitor and refine the model as data changes.

Example: mining sales data finds that customers who buy a laptop often buy a bag within a week, so the shop bundles them.

✓ Answer checked by Claude Opus 5.5 (high effort)

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