数据挖掘双语教学课件9cmplx.pptVIP

Data Mining: Concepts and Techniques Data Mining: Concepts and Techniques — Slides for Textbook — — Chapter 9 — ?Jiawei Han and Micheline Kamber Intelligent Database Systems Research Lab School of Computing Science Simon Fraser University, Canada http://www.cs.sfu.ca Chapter 9. Mining Complex Types of Data Multidimensional analysis and descriptive mining of complex data objects Mining spatial databases Mining multimedia databases Mining time-series and sequence data Mining text databases Mining the World-Wide Web Summary Mining Complex Data Objects: Generalization of Structured Data Set-valued attribute Generalization of each value in the set into its corresponding higher-level concepts Derivation of the general behavior of the set, such as the number of elements in the set, the types or value ranges in the set, or the weighted average for numerical data E.g., hobby = {tennis, hockey, chess, violin, nintendo_games} generalizes to {sports, music, video_games} List-valued or a sequence-valued attribute Same as set-valued attributes except that the order of the elements in the sequence should be observed in the generalization Generalizing Spatial and Multimedia Data Spatial data: Generalize detailed geographic points into clustered regions, such as business, residential, industrial, or agricultural areas, according to land usage Require the merge of a set of geographic areas by spatial operations Image data: Extracted by aggregation and/or approximation Size, color, shape, texture, orientation, and relative positions and structures of the contained objects or regions in the image Music data: Summarize its melody: based on the approximate patterns that repeatedly occur in the segment Summarized its style: based on its tone, tempo, or the major musical instruments played Generalizing Object Data Object identifier: generalize to the lowest level of class in the class/subclass hierarchies Class composition hierarchies generalize nested structured data generalize only