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# Quick Look At Clustering

## on Feb 16, 2010

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Quick Look At Clustering

Quick Look At Clustering

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## Quick Look At ClusteringPresentation Transcript

• CLUSTERING
• Introduction
Centroid - Center of a cluster
Centroid could be either a real point or an imaginary one.
Objective function –
Measures the quality of clustering (small value is desirable)
Calculated by summing the squares of distances of each point from the centroid of the cluster
Two types of Clustering are:
k-means Clustering
Hierarchical Clustering
• k-means Clustering
It is an exclusive clustering algorithm
Algorithm:
Select a value for ‘k’
Select ‘k’ objects in an arbitrary fashion. Use it as an initial set of k centroids
Assign each object to the cluster for which it is nearest to the centroid
Recalculate the centroids
Repeat steps 3 & 4 until centroids don’t move.
It may not find the best set of clusters but will always terminate.
• Agglomerative Hierarchical Clustering
Algorithm:
Assign each object to its own single-object cluster. Calculate the distance between each pair (distance matrix)
Select and merge the closest pairs
Calculate the distance between this new cluster and other clusters.
Repeat steps 2 & 3 until all objects are in single cluster
• Example
Before clustering
After two passes
• Example
It gives the entire hierarchy of clusters
Dendrogram (A Binary tree) – End result of hierarchical clustering
• Distance Measure
Three ways of calculating distances:
Shortest distance from any member of one cluster to any member of another cluster
Longest distance from any member of one cluster to any member of another cluster
Average distance from any member of one cluster to any member of another cluster
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