Data Mining

The areas covered include association analysis, classification, clustering, text mining, web mining, graph and stream time series mining.

What I have learned:

1. Identify the process of data mining and KDD (Knowledge Discovery from Databases).
2. Analyze the applicability of different data mining and KDD algorithms.
3. Design algorithms to solve problems related to classifications and clustering, as well as identify association rules from a database.
4. Apply the concepts and algorithms of text mining, web mining, graph mining and stream and time series mining.
5. Evaluate the performance of data mining and KDD algorithms.
6. Compare and contrast the performances of different data mining algorithms
7. Evaluate the scalability of data mining algorithms.
8. Analyze the data characteristics that affect the effectiveness of data mining.
9. Examine the limitations of data mining and KDD algorithms