What is Clustering in Machine Learning?

Clustering is an unsupervised machine learning method that divides the data into different clusters and places them in separate classes. Unsupervised learning algorithms are those which don’t need any form of labelling on the input data, and there is no human to give feedback. 

Such algorithms are used when patterns and insights need to be extracted from unstructured or semi-structured data which is unlabelled. 

The process of clustering divides the input dataset which is fed to a clustering algorithm into different data points based on how similar these points are to one another. Points which are not similar to one another at all are placed in far off groups whereas similar points are placed in the same class or nearby class. 

Significance of clustering 

It helps in grouping data that is similar in certain aspects together, thereby labelling such data (indirectly). This way, similar data would lie in one class thereby making it easy to perform computations on this specific type of data. 

Clustering algorithms: There are many clustering algorithms and the most widely used algorithm is k-means clustering. Other clustering algorithms include Mean-shift clustering, and Density based spatial clustering of applications with noise (DBSCAN). 

K-means Clustering 

It is one of the simplest and widely used algorithms since it is easy to implement. 

1. The first step is to select a number for the classes/groups into which the data needs to be clustered into. Next, these classes are randomly assigned a center point. 
2. Every data point is classified by determining the distance between that specific point and the center of the group. After this, the point is classified into the group whose center is the closest to it. 
3. Based on this classification, the center of every group is recomputed, wherein the mean of all the vectors in the group is computed. 
4. These steps are repeated for a defined number of iterations or until there are no significant changes between one iteration and the next. 
5. The group centers can be randomly initialized for the first few times and then an iteration can be selected that yielded the best results. 
6. K-means clustering is a fast process and there are very few computations that need to be performed to get results. It has a linear complexity of O(n). 

Disadvantages of k-means clustering 

1. The user has to explicitly select the number of groups/classes into which data needs to be classified into. 
2. Different results are produced based on the randomness of selecting the center of every cluster. 
3. Due to this, the result could be inconsistent. 

Applications of clustering algorithms 

1. In the field of marketing: Clustering algorithms are used to analyze and understand customer segment. 
2. Studying earthquake patterns, thereby helping in the prediction of potential earthquakes. 

Conclusion 

In this post, we understood the meaning and significance of clustering, which is an unsupervised learning algorithm.