K-Means Clustering Algorithm and K-Medoids Clustering Algorithm

K-Means Clustering Algorithm and K-Medoids clustering

K-Means Clustering Algorithm 

K-Means Clustering is an unsupervised learning algorithm that is used to solve the clustering problems in machine learning or data science. In this topic, we will learn what is K-means clustering algorithm, how the algorithm works, along with the Python implementation of k-means clustering.

What is K-Means Algorithm?

K-Means Clustering is an Unsupervised Learning algorithm, which groups the unlabeled dataset into different clusters. Here K defines the number of pre-defined clusters that need to be created in the process, as if K=2, there will be two clusters, and for K=3, there will be three clusters, and so on.

It is an iterative algorithm that divides the unlabeled dataset into k different clusters in such a way that each dataset belongs only one group that has similar properties.

It is a centroid-based algorithm, where each cluster is associated with a centroid. The main aim of this algorithm is to minimize the sum of distances between the data point and their corresponding clusters.

The algorithm takes the unlabeled dataset as input, divides the dataset into k-number of clusters, and repeats the process until it does not find the best clusters. The value of k should be predetermined in this algorithm.

The k-means clustering algorithm mainly performs two tasks:

-Determines the best value for K center points or centroids by an iterative process.

-Assigns each data point to its closest k-center. Those data points which are near to the particular k-center, create a cluster.

Hence each cluster has data points with some commonalities, and it is away from other clusters. 

The below diagram explains the working of the K-means Clustering Algorithm:

K-Medoids clustering-

K-Medoids and K-Means are two types of clustering mechanisms in Partition Clustering. First, Clustering is the process of breaking down an abstract group of data points/ objects into classes of similar objects such that all the objects in one cluster have similar traits. , a group of n objects is broken down into k number of clusters based on their similarities.

Two statisticians, Leonard Kaufman, and Peter J. Rousseeuw came up with this method. This tutorial explains what K-Medoids do, their applications, and the difference between K-Means and K-Medoids.

K-medoids is an unsupervised method with unlabelled data to be clustered. It is an improvised version of the K-Means algorithm mainly designed to deal with outlier data sensitivity. Compared to other partitioning algorithms, the algorithm is simple, fast, and easy to implement.

K-Medoids:

Medoid: A Medoid is a point in the cluster from which the sum of distances to other data points is minimal.

(or)

A Medoid is a point in the cluster from which dissimilarities with all the other points in the clusters are minimal.

Instead of centroids as reference points in K-Means algorithms, the K-Medoids algorithm takes a Medoid as a reference point.

There are three types of algorithms for K-Medoids Clustering:

1.PAM (Partitioning Around Clustering)

2.CLARA (Clustering Large Applications)

3.CLARANS (Randomized Clustering Large Applications)

Read More-

1. Data Warehouse and Data Mining All Chapter Notes
2. What Is Data Warehouse
3. Applications of Data Warehouse, Types Of Data Warehouse
4. Architecture of Data Warehousing
5. Difference Between OLTP And OLAP
6. Python Notes