Hierarchical Clustering
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In data mining, hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types: *Agglomerative: This is a bottom up approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. *Divisive: This is a top down approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy. In general, the merges and splits are determined in a greedy algorithm|greedy manner. The results of hierarchical clustering are usually presented in a dendrogram. In the general case, the complexity of agglomerative clustering is \mathcal{O}(n^3), which makes them too slow for large data sets. Divisive clustering with an exhaustive search is \mathcal{O}(2^n), which is even worse. However, for some special cases, optimal efficient agglomerative methods (of complexity \mathcal{O}(n^2)) are known: SLINK...
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K Means    
Data Mining    
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