Clustering in Machine Learning: 3 Types of Clustering Explained

Clustering in Machine Learning: 3 Types of Clustering Explained

In this article, we are going to learn the need of clustering, different types of clustering along with their pros and cons.

Introduction

Machine Learning is one of the hottest technologies in 2020, as the data is increasing day by day the need of Machine Learning is also increasing exponentially. Machine Learning is a very vast topic that has different algorithms and use cases in each domain and Industry. One of which is Unsupervised Learning in which we can see the use of Clustering.

Unsupervised learning is a technique in which the machine learns from unlabeled data. As we do not know the labels there is no right answer given for the machine to learn from it, but the machine itself finds some patterns out of the given data to come up with the answers to the business problem.

Clustering is a Machine Learning Unsupervised Learning technique that involves the grouping of given unlabeled data. In each cleaned data set, by using Clustering Algorithm we can cluster the given data points into each group. The clustering Algorithm assumes that the data points that are in the same cluster should have similar properties, while data points in different clusters should have highly dissimilar properties.

In this article, we are going to learn the need of clustering, different types of clustering along with their pros and cons.

What is the need of Clustering?

Clustering is a widely used ML Algorithm which allows us to find hidden relationships between the data points in our dataset.

Examples:

1)    Customers are segmented according to similarities of the previous customers and can be used for recommendations.

2)    Based on a collection of text data, we can organize the data according to the content similarities in order to create a topic hierarchy.

3)    Image processing mainly in biology research for identifying the underlying patterns.

4)    Spam filtering.

5)    Identifying Fraudulent and Criminal activities.

6)    It can also be used for fantasy football and sports.

*Types of Clustering     *

There are many types of Clustering Algorithms in Machine learning. We are going to discuss the below three algorithms in this article:

1)    K-Means Clustering.

2)    Mean-Shift Clustering.

3)    DBSCAN.

1. K-Means Clustering

K-Means is the most popular clustering algorithm among the other clustering algorithms in Machine Learning. We can see this algorithm used in many top industries or even in a lot of introduction courses. It is one of the easiest models to start with both in implementation and understanding.

Step-1 We first select a random number of k to use and randomly initialize their respective center points.

Step-2 Each data point is then classified by calculating the distance (Euclidean or Manhattan) between that point and each group center, and then clustering the data point to be in the cluster whose center is closest to it.

Step-3 We recompute the group center by taking the mean of all the vectors in the group.

Step-4 We repeat all these steps for a n number of iterations or until the group centers don’t change much.

artificial intelligence clustering

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