DM13 Clustering - Data Mining, Analytics, Big Data, and Data 21聚类数据挖掘，分析，大的数据，和数据.pptVIP

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DM13 Clustering - Data Mining, Analytics, Big Data, and Data 21聚类数据挖掘，分析，大的数据，和数据.ppt

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DM13 Clustering - Data Mining, Analytics, Big Data, and Data 21聚类数据挖掘，分析，大的数据，和数据

* *Overfitting-avoidance heuristic If every instance gets put into a different category the numerator becomes (maximal): Where n is number of all possible attribute values. So without k in the denominator of the CU-formula, every cluster would consist of one instance! Maximum value of CU * Other Clustering Approaches EM – probability based clustering Bayesian clustering SOM – self-organizing maps … * Discussion Can interpret clusters by using supervised learning learn a classifier based on clusters Decrease dependence between attributes? pre-processing step E.g. use principal component analysis Can be used to fill in missing values Key advantage of probabilistic clustering: Can estimate likelihood of data Use it to compare different models objectively * Examples of Clustering Applications Marketing: discover customer groups and use them for targeted marketing and re-organization Astronomy: find groups of similar stars and galaxies Earth-quake studies: Observed earth quake epicenters should be clustered along continent faults Genomics: finding groups of gene with similar expressions … * Clustering Summary unsupervised many approaches K-means – simple, sometimes useful K-medoids is less sensitive to outliers Hierarchical clustering – works for symbolic attributes Evaluation is a problem * * * Clustering * Outline Introduction K-means clustering Hierarchical clustering: COBWEB * Classification vs. Clustering Classification: Supervised learning: Learns a method for predicting the instance class from pre-labeled (classified) instances * Clustering Unsupervised learning: Finds “natural” grouping of instances given un-labeled data * Clustering Methods Many different method and algorithms: For numeric and/or symbolic data Deterministic vs. probabilistic Exclusive vs. overlapping Hierarchical vs. flat Top-down vs. bottom-up * Clusters: exclusive vs. overlapping Simple 2-D representation Non-overlapping Venn diagram Overlapping a k j i h g f e d c b