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DATA WAREHOUSING AND DATA MINING - PSU - Intranet数据仓库和数据挖掘-电源-网
* * * * * * * Absolute: 40 M$ 40M$, expected to grow 10 times by 2000 --Forrester research * * Littl e integration: here are few exceptions People are starting to wake up to this possibility and here are some examples I have found by web-surfing. . decision tree most common. Information Discovery claimed to be only serious integrator [DBMS Ap ‘98] Clustering used by some to define new product hierarchies. Of course, rich set of time-series functions especially for forecasting was always there New charting software: 80/20, A-B-C analysis, quadrant plotting. Univ. Jiawen Han. Previous approach has been to bring in mining operations in olap. Look at mining operations and choose what fits. My approach has been to reflect on what people do with cube metaphor and the drill-down, roll-up, based exploration and see if there is anything there that can be automated. Discuss my work first. * * Angoss Software Corp. was formed under the Business Corp. Act (ontario) in 1980. It began data mining software production in 1992. It is publicly traded on the Canadian Venture Exchange under the trading symbol “ANC” Promote the rapid knowledge transfer to customers in the use of technology and adoption of “best practice” for data mining * * * * * * * * Here it’s obviously the algorithms. * Here it’s less clear – maybe it’s the algorithms, but more it’s the attitude… * * * * * How it works How it’s really used. Handling of business problems and algorithms / expert features Deep embedding of deployment CRISP-DM pane * * In the previous lesson we discussed Classification using decision trees. Sometimes decision trees are inconvenient – they can be very large Also, they require starting at the same attribute We can extract modular “nuggets” of knowledge by getting rules * * Classification: Train, Validation, Test split Data Predictions Y N Results Known Training set Validation set + + - - + Model Builder Evaluate + - + - Final Model Final Test Set + - + - Final Evaluatio
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