chap07_new - Cluster Analysis(Clustering Jiawei Han and...

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二〇一七年五月三十一日 Data Mining: Concepts and Techniques 1 Cluster Analysis ( Clustering ) © Jiawei Han and Micheline Kamber Intelligent Database Systems Research Lab School of Computing Science Simon Fraser University, Canada
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二〇一七年五月三十一日 Data Mining: Concepts and Techniques 2 Chapter 7. Cluster Analysis Outline What is Cluster Analysis? Types of Data in Cluster Analysis Major Clustering Methods Partitioning Methods Hierarchical Methods Density-Based Methods Grid-Based Methods Model-Based Clustering Methods Summary
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二〇一七年五月三十一日 Data Mining: Concepts and Techniques 4 General Applications of Clustering ( Exploratory Analysis ) Business/Economic Science (especially marketing research) Biomedical Informatics Pattern Recognition Spatial Data Analysis detect spatial clusters and explain them in spatial data mining WWW Web document profiling Cluster log data to discover groups of similar access patterns Domain Knowledge is very, very important to the applications of clustering !!
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二〇一七年五月三十一日 Data Mining: Concepts and Techniques 5 Specific Examples of Clustering Applications Marketing: Help marketers discover group profiles of customers, and then use this knowledge to develop targeted marketing programs Insurance: Identifying group profiles of motor insurance policy holders with a high average claim cost Biology: Categorize genes with similar functionality, derive their taxonomies, and gain insight into the structures inherent in populations
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二〇一七年五月三十一日 Data Mining: Concepts and Techniques 6 What Is Good Clustering? A good clustering method will produce high quality clusters with the following features high intra-class similarity low inter-class similarity The quality of a clustering result depends on both the similarity measure and the clustering method . The quality of a clustering method is measured by its ability to discover the number of the hidden patterns.
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二〇一七年五月三十一日 Data Mining: Concepts and Techniques 7 Issues of Clustering in Data Mining Scalability (important for Big Data Analysis) Ability to deal with different types of attributes Discovery of clusters with arbitrary shape Minimal requirements for domain knowledge to determine input parameters Able to deal with noise and outliers Insensitive to order of input records Able to deal with high dimensionality Incorporation of user-specified constraints Interpretability and usability
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二〇一七年五月三十一日 Data Mining: Concepts and Techniques 8 Chapter 7. Cluster Analysis What is Cluster Analysis?
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