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Main requirements of data mining

Short answer

Main requirements are handling different data types, efficiency and scalability, data quality handling, multiple mining functions, mining at multiple abstraction levels, interactive mining, incorporation of background knowledge, integration with databases, understandable results, a mining query language, and privacy and security.

What are the main requirements of data mining?

The main requirements (and challenges) a data mining system must meet are:

  1. Handling of different data types: relational, transactional, text, spatial, multimedia, time-series and web data.
  2. Efficiency and scalability: algorithms must run in acceptable time on very large data sets, possibly with parallel and distributed processing.
  3. Data quality handling: noisy, incomplete and inconsistent data must be handled, with outlier detection.
  4. Multiple mining functions: characterisation, association, classification, clustering, prediction and outlier analysis, usable together.
  5. Mining at multiple levels of abstraction: drill-down and roll-up on discovered knowledge, using concept hierarchies.
  6. Interactive mining: the user can refine mining requests and thresholds on the fly.
  7. Incorporation of background (domain) knowledge.
  8. Integration with databases and warehouses and with query languages (e.g. DMQL).
  9. Understandable results: patterns must be interesting, valid and presented clearly through visualisation.
  10. Data mining query language and ad hoc mining.
  11. Privacy, security and social issues: protect personal data.

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