5. Presentation and visualization of data mining
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
Pattern may be uninteresting to the user.
Solve by user specified constraints.
• Efficiency and scalability of data mining algorithms.
Should be opt for huge amount of data.
• Parallel, Distributed and incremental mining
Huge size of database
Wide distribution of data
Data mining methods
Solve by; efficient algorithms.
Diversity of data Types Issues
• Handling of relational and complex types of
One system-> to mine all kinds of data
Specific data mining system should be
• Mining information from heterogeneous
databases and global information systems.
Web mining uncover knowledge about web
contents, web structure, web usage and web