- How is K means clustering used?
- What are the advantages and disadvantages of K means clustering?
- Why is K means better?
- What is Pam algorithm?
- Does K mean supervised?
- What is the K Medoids method?
- Which method is more robust K means or K Medoids?
- What is K means algorithm with example?
- How do you calculate K mean?
- What is the output of K means?
- What is difference between K means and K Medoids?

## How is K means clustering used?

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 are the advantages and disadvantages of K means 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.

## Why is K means better?

K-means has been around since the 1970s and fares better than other clustering algorithms like density-based, expectation-maximisation. It is one of the most robust methods, especially for image segmentation and image annotation projects. According to some users, K-means is very simple and easy to implement.

## What is Pam algorithm?

The PAM Clustering Algorithm. PAM stands for “partition around medoids”. The algorithm is intended to find a sequence of objects called medoids that are centrally located in clusters. Objects that are tentatively defined as medoids are placed into a set S of selected objects.

## Does K mean supervised?

K-means is a clustering algorithm that tries to partition a set of points into K sets (clusters) such that the points in each cluster tend to be near each other. … It is supervised because you are trying to classify a point based on the known classification of other points.

## 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).

## Which method is more robust K means or K Medoids?

K- Medoids is more robust as compared to K-Means as in K-Medoids we find k as representative object to minimize the sum of dissimilarities of data objects whereas, K-Means used sum of squared Euclidean distances for data objects. And this distance metric reduces noise and outliers.

## What is K means algorithm with example?

K-means clustering algorithm computes the centroids and iterates until we it finds optimal centroid. It assumes that the number of clusters are already known. It is also called flat clustering algorithm. The number of clusters identified from data by algorithm is represented by ‘K’ in K-means.

## How do you calculate K mean?

Introduction to K-Means ClusteringStep 1: Choose the number of clusters k. … Step 2: Select k random points from the data as centroids. … Step 3: Assign all the points to the closest cluster centroid. … Step 4: Recompute the centroids of newly formed clusters. … Step 5: Repeat steps 3 and 4.

## What is the output of K means?

The K-Means Operator output is simply the assignment of the data members to K number of clusters, or groupings. Unlike the Regression or Decision Tree/CART Operators, the K-Means Operator does not provide a final “answer” or prediction. …

## What is 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).