[工学]韩家炜数据挖掘PPT07.ppt

Data Mining: Concepts and Techniques Data Mining:  Concepts and Techniques — Chapter 7 — Jiawei Han Department of Computer Science University of Illinois at Urbana-Champaign /~hanj ?2006 Jiawei Han and Micheline Kamber, All rights reserved Chapter 7. Cluster Analysis What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods Hierarchical Methods Density-Based Methods Grid-Based Methods Model-Based Methods Clustering High-Dimensional Data Constraint-Based Clustering Outlier Analysis Summary Chapter 7. Cluster Analysis What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods Hierarchical Methods Density-Based Methods Grid-Based Methods Model-Based Methods Clustering High-Dimensional Data Constraint-Based Clustering Outlier Analysis Summary What is Cluster Analysis? Cluster: a collection of data objects Similar to one another within the same cluster Dissimilar to the objects in other clusters Cluster analysis Finding similarities between data according to the characteristics found in the data and grouping similar data objects into clusters Unsupervised learning: no predefined classes Typical applications As a stand-alone tool to get insight into data distribution As a preprocessing step for other algorithms Clustering: Rich Applications and Multidisciplinary Efforts Pattern Recognition Spatial Data Analysis Create thematic maps in GIS by clustering feature spaces Detect spatial clusters or for other spatial mining tasks Image Processing Economic Science (especially market research) WWW Document classification Cluster Weblog data to discover groups of similar access patterns Examples of Clustering Applications Marketing: Help marketers discover distinct groups in their customer bases, and then use this knowledge to develop targeted marketing programs Land use: Identification of areas of similar land use in an earth observat