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K-Means Clustering Overview

 

Overview

K-Means clustering generates a specific number of disjoint, flat (non-hierarchical) clusters. It is well suited to generating globular clusters. The K-Means method is numerical, unsupervised, non-deterministic and iterative.

 

K-Means Algorithm Properties

 

The K-Means Algorithm Process

 

K-Means Clustering in GeneLinkerô

The version of the K-Means algorithm used in GeneLinkerô differs from the conventional K-Means algorithm in that GeneLinkerô does not compute the centroid of the clusters to measure the distance from a data point to a cluster. Instead, the algorithm uses a specified linkage distance metric. The use of the Average Linkage distance metric most closely corresponds to conventional K-Means, but can produce different results in many cases.

 

Advantages to Using this Technique

 

Disadvantages to Using this Technique

 

Note the Warning in Pearson Correlation and Pearson Squared Distance Metric on use of K-Means clustering.

 

Related Topics:

Performing K-Means Clustering

Clustering Overview

Distance Metrics Overview