Clustering in Machine Learning

Hello Everyone.....!!!

Here is my article on the topic "Clustering in Machine Learning" as a part of our peer subject ML .

Introduction  

It is basically a type of unsupervised learning method. An unsupervised learning method is a method in which we draw references from datasets consisting of input data without labeled responses. Generally, it is used as a process to find meaningful structure, explanatory underlying processes, generative features, and groupings inherent in a set of examples. 

Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. 

For ex– The data points in the graph below clustered together can be classified into one single group. We can distinguish the clusters, and we can identify that there are 3 clusters in the below picture. 

Why Clustering? 

Clustering is very much important as it determines the intrinsic grouping among the unlabelled data present. There are no criteria for good clustering. It depends on the user, what is the criteria they may use which satisfy their need.

For instance, we could be interested in finding representatives for homogeneous groups (data reduction), in finding “natural clusters” and describe their unknown properties (“natural” data types), in finding useful and suitable groupings (“useful” data classes) or in finding unusual data objects (outlier detection).

This algorithm must make some assumptions that constitute the similarity of points and each assumption make different and equally valid clusters. 

Clustering Methods : 

• Density-Based Methods: These methods consider the clusters as the dense region having some similarities and differences from the lower dense region of the space. These methods have good accuracy and the ability to merge two clusters.
• Hierarchical Based Methods: The clusters formed in this method form a tree-type structure based on the hierarchy. New clusters are formed using the previously formed one. It is divided into two category 
• Agglomerative (bottom-up approach)
• Divisive (top-down approach)

Clustering Algorithms:

One Of the important Clustering Algorithm is K-means clustering

K-means clustering algorithm – It is the simplest unsupervised learning algorithm that solves clustering problem. K-means algorithm partitions n observations into k clusters where each observation belongs to the cluster with the nearest mean serving as a prototype of the cluster. It is fast with fewer computations required, with the linear complexity of O(n).

Uses Of Clustering

The clustering technique can be widely used in various tasks. Some most common uses of this technique are:

• Market Segmentation
• Statistical data analysis
• Social network analysis
• Image segmentation
• Anomaly detection, etc.

Apart from these general usages, it is used by the Amazon in its recommendation system to provide the recommendations as per the past search of products. Netflix also uses this technique to recommend the movies and web-series to its users as per the watch history.

The below diagram explains the working of the clustering algorithm. We can see the different fruits are divided into several groups with similar properties.

Applications of Clustering in different fields  

• Marketing: It can be used to characterize & discover customer segments for marketing purposes.
• Biology: It can be used for classification among different species of plants and animals.
• In Search Engines: Search engines also work on the clustering technique. The search result appears based on the closest object to the search query. It does it by grouping similar data objects in one group that is far from the other dissimilar objects. The accurate result of a query depends on the quality of the clustering algorithm used.
• Earthquake studies: By learning the earthquake-affected areas we can determine the dangerous zones. 

Conclusion:

Through this article I think you have got to know about Clustering .Thank you