## What is the objective function of k Medoid algorithm?

The objective function corresponds to the sum of the dissimilarities of all objects to their nearest medoid. The SWAP step attempts to improve the quality of the clustering by exchanging selected objects (medoids) and non-selected objects.

**What are the advantages of K Medoids over k-means?**

“It [k-medoid] is more robust to noise and outliers as compared to k-means because it minimizes a sum of pairwise dissimilarities instead of a sum of squared Euclidean distances.”

**What is K-means clustering used for?**

The K-means clustering algorithm is used to find groups which have not been explicitly labeled in the data. This can be used to confirm business assumptions about what types of groups exist or to identify unknown groups in complex data sets.

### What is K Medoid data mining?

k -medoids is a classical partitioning technique of clustering that splits the data set of n objects into k clusters, where the number k of clusters assumed known a priori (which implies that the programmer must specify k before the execution of a k -medoids algorithm).

**What is k-means and k-medoids clustering explain with help of an example?**

Both the k -means and k -medoids algorithms are partitional (breaking the dataset up into groups). K-means attempts to minimize the total squared error, while k-medoids minimizes the sum of dissimilarities between points labeled to be in a cluster and a point designated as the center of that cluster.

**What are the advantages and disadvantages of K-means clustering against model based clustering?**

1) If variables are huge, then K-Means most of the times computationally faster than hierarchical clustering, if we keep k smalls. 2) K-Means produce tighter clusters than hierarchical clustering, especially if the clusters are globular. K-Means Disadvantages : 1) Difficult to predict K-Value.

## What are the most practical applications of k-means?

Applications of K-Means Clustering: such as document clustering, identifying crime-prone areas, customer segmentation, insurance fraud detection, public transport data analysis, clustering of IT alertsâ€¦etc.

**Where can we apply clustering algorithm in real life?**

5 Examples of Cluster Analysis in Real Life

- Example 1: Retail Marketing.
- Example 2: Streaming Services.
- Example 3: Sports Science.
- Example 4: Email Marketing.
- Example 5: Health Insurance.
- Additional Resources.