8-1-数据仓库与数据挖掘.ppt

* * * * * * * * * * * * * * Clustering Algorithms Clustering algorithms have been designed to handle very large datasets E.g., the Birch algorithm Main idea: use an in-memory R-tree to store points that are being clustered Insert points one at a time into the R-tree, merging a new point with an existing cluster if is less than some ? distance away If there are more leaf nodes than fit in memory, merge existing clusters that are close to each other At the end of first pass we get a large number of clusters at the leaves of the R-tree Merge clusters to reduce the number of clusters Collaborative Filtering Goal: predict what movies/books/… a person may be interested in, on the basis of Past preferences of the person Other people with similar past preferences The preferences of such people for a new movie/book/… One approach based on repeated clustering Cluster people on the basis of preferences for movies Then cluster movies on the basis of being liked by the same clusters of people Again cluster people based on their preferences for (the newly created clusters of) movies Repeat above till equilibrium Above problem is an instance of collaborative filtering, where users collaborate in the task of filtering information to find information of interest Other Types of Mining Text mining: application of data mining to textual documents cluster Web pages to find related pages cluster pages a user has visited to organize their visit history classify Web pages automatically into a Web directory Data visualization systems help users examine large volumes of data and detect patterns visually Can visually encode large amounts of information on a single screen Humans are very good a detecting visual patterns End of Chapter Figure 20.01 Figure 20.02 Figure 20.03 Figure 20.05 * * * * * * * * * * * * * * * * * * * * * * * * ?Silberschatz, Korth and Sudarshan 20.* Database System Concepts - 6th Edition Database System Concepts, 6th Ed. ?Silberschatz, Korth and SudarshanSee for condit