KMeans Clustering in data mining

What is clustering

Clustering is a process of partitioning a group of data into small partitions or cluster on the basis of similarity and dissimilarity.

What is K-Means clustering in data mining?

K-Means clustering is a clustering method in which we move the every data item(attribute value) nearest to its similar cluster.

How does K-means clustering work?

Step 1:

Find the centroid randomly. It is better to take the boundary and middle values as the centroid.

Step 2:

Assign cluster to each item-set(value of the attribute)

Step 3:

Repeat all the process, every time we repeat the process total sum of error rate is changed. When the error rate stops to change, then finalize the cluster and their itemset(attribute value).

Iteration 2:

Iteration 3: 

Iteration 4:

Iteration stops:

Now, iterations are stopped because the error rate is consistent with iteration 3 and iteration 4. The error rate is now fixed at 220.75, so there is no need for further. Clusters are final now.

Shortcomings of K-Means clustering

1. It is sensitive to outliers.
2. Not much suitable for categorical or nominal data.

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