数据挖掘双语教学课件4lang幻灯片.pptVIP

* Data Mining: Concepts and Techniques * Data Mining: Concepts and Techniques — Slides for Textbook — — Chapter 4 — ?Jiawei Han and Micheline Kamber Intelligent Database Systems Research Lab School of Computing Science Simon Fraser University, Canada http://www.cs.sfu.ca * Data Mining: Concepts and Techniques * Chapter 4: Data Mining Primitives, Languages, and System Architectures Data mining primitives: What defines a data mining task? A data mining query language Design graphical user interfaces based on a data mining query language Architecture of data mining systems Summary * Data Mining: Concepts and Techniques * Why Data Mining Primitives and Languages? Finding all the patterns autonomously in a database? — unrealistic because the patterns could be too many but uninteresting Data mining should be an interactive process User directs what to be mined Users must be provided with a set of primitives to be used to communicate with the data mining system Incorporating these primitives in a data mining query language More flexible user interaction Foundation for design of graphical user interface Standardization of data mining industry and practice * Data Mining: Concepts and Techniques * What Defines a Data Mining Task ? Task-relevant data Type of knowledge to be mined Background knowledge Pattern interestingness measurements Visualization of discovered patterns * Data Mining: Concepts and Techniques * Task-Relevant Data (Minable View) Database or data warehouse name Database tables or data warehouse cubes Condition for data selection Relevant attributes or dimensions Data grouping criteria * Data Mining: Concepts and Techniques * Types of knowledge to be mined Characterization Discrimination Association Classification/prediction Clustering Outlier analysis Other data mining tasks * Data Mining: Concepts and Techniques * Background Knowledge: Concept Hierarchies Schema hierarchy E.g., street city province_or_state country Set-grouping hierarchy E.g., {20-3