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class11 - HierarchicalClustering:TimeandSpacerequirements q...

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Hierarchical Clustering:  Time and Space requirements O(N 2 ) space since it uses the proximity matrix. N is the number of points. O(N 3 ) time in many cases There are N steps and at each step the size, N 2 , proximity matrix must be updated and searched Complexity can be reduced to O(N 2 log(N) ) time for some approaches
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Hierarchical Clustering:  Problems and Limitations Once a decision is made to combine two clusters, it cannot be undone No objective function is directly minimized Different schemes have problems with one or more of the following: Sensitivity to noise and outliers Difficulty handling different sized clusters and convex shapes Breaking large clusters
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MST: Divisive Hierarchical Clustering Build MST (Minimum Spanning Tree) Start with a tree that consists of any point In successive steps, look for the closest pair of points (p, q) such that one point (p) is in the current tree but the other (q) is not Add q to the tree and put an edge between p and q
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MST: Divisive Hierarchical Clustering Use MST for constructing hierarchy of clusters
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DBSCAN DBSCAN is a density-based algorithm.
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This note was uploaded on 08/29/2011 for the course CAP 4770 taught by Professor Staff during the Fall '08 term at FIU.

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class11 - HierarchicalClustering:TimeandSpacerequirements q...

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