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Concept of Variance in Decision making

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Chapter 7. Cluster Analysis 1. What is Cluster Analysis? 2. Types of Data in Cluster Analysis 3. A Categorization of Major Clustering Methods 4. Partitioning Methods 5. Hierarchical Methods 6. Density-Based Methods 7. Grid-Based Methods 8. Model-Based Methods 9. Clustering High-Dimensional Data 10. Constraint-Based Clustering 11. Outlier Analysis 12. Summary October 16, T. MUTHAMILSELVA 1 What is Cluster Analysis?   Cluster: The process of grouping a set of physical or abstract objects into classes of similar objects is called clustering.  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 used for outlier detection- more interesting than common cases October 16, T. MUTHAMILSELVA  2 Clustering: Rich Applications and Multidisciplinary Efforts  Pattern Recognition  Spatial Data Analysis  Image Processing  Economic Science (especially market research)  WWW  Document classification  Cluster Weblog data to discover groups of similar access patterns October 16, T. MUTHAMILSELVA 3 Requirements of Clustering in Data Mining  Scalability  Ability to deal with different types of attributes  Discovery of clusters with arbitrary shape  Minimal requirements for ...
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