Major issues in data mining

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Major issues in data mining

  1. 1. Major Issues in Data Mining V. Saranya AP/CSE Sri Vidya College of Engineering & Technology, Virudhunagar
  2. 2. • Issues – Mining Methodology – User interaction – Performance – Data types.
  3. 3. Mining Methodology & User Interaction Issues 1. Mining different kinds of knowledge in database.  Different users-different knowledge-different way (with same database)
  4. 4. 2. Interactive Mining of knowledge at multiple levels of abstraction.  Focus the search patterns.  Different angles.
  5. 5. 4. Data mining query languages and ad hoc data mining  High level data mining query language  Conditions and constraints.
  6. 6. 3. Incorporation of background knowledge.  Background & Domain knowledge.
  7. 7. 5. Presentation and visualization of data mining results.  Use visual representations.  Expressive forms like graph, chart, matrices, curves, tables, etc… 6. Handling noisy or incomplete data.  Confuse the process  Over fit the data (apply any outlier analysis, data cleaning methods) 7.Pattern evaluation- the interestingness problem.  Pattern may be uninteresting to the user.  Solve by user specified constraints.
  8. 8. Performance Issues • Efficiency and scalability of data mining algorithms. Running time. Should be opt for huge amount of data. • Parallel, Distributed and incremental mining algorithms. Huge size of database Wide distribution of data High cost Computational complexity Data mining methods Solve by; efficient algorithms.
  9. 9. Diversity of data Types Issues • Handling of relational and complex types of data. One system-> to mine all kinds of data Specific data mining system should be constructed. • Mining information from heterogeneous databases and global information systems.  Web mining uncover knowledge about web contents, web structure, web usage and web dynamics

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