Major issues in data mining

Major issues in data mining

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  Major Issues inData Mining V. Saranya AP/CSE Sri Vidya College of Engineering & Technology, Virudhunagar
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  • Issues – MiningMethodology – User interaction – Performance – Data types.
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  Mining Methodology &User Interaction Issues 1. Mining different kinds of knowledge in database.  Different users-different knowledge-different way (with same database)
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  2. Interactive Miningof knowledge at multiple levels of abstraction.  Focus the search patterns.  Different angles.
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  4. Data miningquery languages and ad hoc data mining  High level data mining query language  Conditions and constraints.
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  3. Incorporation ofbackground knowledge.  Background & Domain knowledge.
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  5. Presentation andvisualization 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.
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  Performance Issues • Efficiencyand 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.
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  Diversity of dataTypes 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