# What is k-means and k-medoids?

## What is 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).

**Is Pam and k-medoids same?**

The difference is in new medoid selection (per iteration): K-medoids selects object that is closest to the medoid as a next medoid. PAM tries out all of the objects in the cluster as a new medoid that will lead to lower SSE.

### What is k-means Plus Plus?

The k-means method is a widely used clustering technique that seeks to minimize the average squared distance between points in the same cluster. Given an integer k and a set of n data points in Rd, the goal is to choose k centers so as to minimize φ, the total squared distance between each point and its closest center.

**Which is faster k-means or K medoids?**

K means is quite fast and less expensive than k medoid. K medoid computes all the pairwise distances, it is O(n^2*k*i), k-means runs in O(n*k*i), k times the number of iterations is k*i << n. Hope this answer helps.

#### What are the advantages and disadvantages of K medoids clustering?

K Meloid clustering is an algorithm based on partition. Its advantages are that it can solve K- means problems and produce empty clusters and is sensitive to outliers or noise. It also selects the most centered member belonging to the cluster. Its disadvantages are that it requires precision and is complex enough.

**What is the difference between centroid and Medoid?**

Medoids are similar in concept to means or centroids, but medoids are always restricted to be members of the data set. Medoids are most commonly used on data when a mean or centroid cannot be defined, such as graphs.

## What is the complexity of K-means algorithm?

Abstract: The k-means algorithm is known to have a time complexity of O(n 2 ), where n is the input data size. This quadratic complexity debars the algorithm from being effectively used in large applications. This process also results in an improved visualization of clustered data.

**Which is faster K means or K Medoids?**

### Why K-means++ is better?

K-means can give different results on different runs. The k-means++ paper provides monte-carlo simulation results that show that k-means++ is both faster and provides a better performance, so there is no guarantee, but it may be better.

**What are the advantages and disadvantages of K Medoid algorithm?**

#### Why K-Medoid is preferred over K mean?

“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.” Here’s an example: Suppose you want to cluster on one dimension with k=2.