Table of Contents

## What is PAM function in R?

The R function pam() [cluster package] can be used to compute PAM algorithm. The simplified format is pam(x, k), where “x” is the data and k is the number of clusters to be generated. After, performing PAM clustering, the R function fviz_cluster() [factoextra package] can be used to visualize the results.

### What is the K Medoids method?

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 PAM in clustering?

PAM stands for “partition around medoids”. The algorithm is intended to find a sequence of objects called medoids that are centrally located in clusters.

**What is the difference between K-means and K Medoids?**

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. In contrast to the k -means algorithm, k -medoids chooses datapoints as centers ( medoids or exemplars).

**What are medians and medoids?**

Note that a medoid is not equivalent to a median or a geometric median. A median is only defined on 1-dimensional data, and it only minimizes dissimilarity to other points for a specific distance metric. A geometric median is defined in any dimension, but is not necessarily a point from within the original dataset.

## What is medoid in data mining?

Medoids are representative objects of a data set or a cluster within a data set whose sum of dissimilarities to all the objects in the cluster is minimal. Medoids are similar in concept to means or centroids, but medoids are always restricted to be members of the data set.

### Is K median and k-medoids same?

If your distance is squared Euclidean distance, use k-means. If your distance is Taxicab metric, use k-medians. If you have any other distance, use k-medoids.

#### What is the output of k-medoids?

k-medoid is based on medoids (which is a point that belongs to the dataset) calculating by minimizing the absolute distance between the points and the selected centroid, rather than minimizing the square distance. As a result, it’s more robust to noise and outliers than k-means.

**What is Pam method?**

The PAM algorithm searches for k representative objects in a data set (k medoids) and then assigns each object to the closest medoid in order to create clusters. Its aim is to minimize the sum of dissimilarities between the objects in a cluster and the center of the same cluster (medoid).

**What is medoids in data mining?**

## How are medoids calculated?

Let the randomly selected 2 medoids, so select k = 2 and let C1 -(4, 5) and C2 -(8, 5) are the two medoids. Step 2: Calculating cost. The dissimilarity of each non-medoid point with the medoids is calculated and tabulated: Each point is assigned to the cluster of that medoid whose dissimilarity is less.

### How do you select K in medoids?

Working of the K-Medoids approach Randomly choose ‘k’ points from the input data (‘k’ is the number of clusters to be formed). The correctness of the choice of k’s value can be assessed using methods such as silhouette method.