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In pattern recognition, the ** k-nearest neighbors algorithm** (

All others are clustering methods.

Option 3.

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k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining.

2)Self-Organizing Map Self Organizing Map (SOM) provides a data visualization technique. SOM also represents clustering concept by grouping similar data together.

3)k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression.

4)Agglomerative hierarchical clustering is a bottom-up clustering method where clusters have sub-clusters, which in turn have sub-clusters, etc.

Hence k nearest neighbour method is not used clustering.

Option C is correct.

2)Self-Organizing Map Self Organizing Map (SOM) provides a data visualization technique. SOM also represents clustering concept by grouping similar data together.

3)k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression.

4)Agglomerative hierarchical clustering is a bottom-up clustering method where clusters have sub-clusters, which in turn have sub-clusters, etc.

Hence k nearest neighbour method is not used clustering.

Option C is correct.