TYPES OF CLUSTERING ALGORITHMS

 

TYPES OF CLUSTERING ALGORITHMS

 

Introduction

Clustering is used to identify groups of similar objects in datasets with two or more variable quantities. In practice, this data may be collected from marketing, biomedical, or geospatial databases, among many other places.

 

What is clustering algorithm?

The clustering algorithm is an unsupervised method, where the input is not a labeled one and problem solving is based on the experience that the algorithm gains out of solving similar problems as a training schedule.

 

Different types of clustering algorithms in machine learning

1.     Gaussian Mixture Models (GMM): GMM assumes that the data points are generated from a mixture of Gaussian distributions. It learns the parameters of these distributions, including the means and covariances, to identify clusters. GMM can assign probabilities of belonging to each cluster.

 

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2.     K-means Clustering: This is one of the most widely used clustering algorithms. It partitions data into K clusters, where each data point belongs to the cluster with the nearest mean (centroid). K-means aims to minimize the within-cluster variance.

 

 

 

 

 

 

 

 

 

3.     Hierarchical Clustering: This algorithm creates a hierarchy of clusters by either agglomerative (bottom-up) or divisive (top-down) approaches. Agglomerative clustering starts with each data point as a separate cluster and iteratively merges the closest clusters, while divisive clustering starts with one cluster containing all data points and recursively splits it.

 

 

4.     Spectral Clustering: Spectral clustering combines data points' affinity matrix with spectral decomposition techniques. It views clustering as a graph partitioning problem and utilizes the eigenvectors of the affinity matrix to cluster the data.

 

5.     Mean Shift Clustering: This algorithm iteratively shifts the centroids of clusters towards the densest regions of data points. It does not require specifying the number of clusters in advance and can discover clusters of varying shapes and sizes.

 

 

Conclusion:

Each Clustering algorithm has its own strengths and weaknesses. The choice of algorithm depends on the nature of the data and the specific requirements of the problem at hand.

 

 

References

·       https://www.geeksforgeeks.org/different-types-clustering-algorithm/

·       https://www.google.com/search?q=clustering+algorithms&oq=clustering+&aqs=chrome.1.69i57j0i20i131i263i433i512j0i20i263i512j0i512l2j0i433i512j0i512l4.204272261j0j15&sourceid=chrome&ie=UTF-8

 

 

Narsima Ahmed

@INTERNATIONAL SCHOOL OF MANAGEMENT EXCELLENCE

Intern @Hunnarvi Technologies under guidance of Nanobi data and analytics pvt ltd.

Views are personal.

#clustering#types #clusteringalgorithm #nanobi #hunnarvi #ISME

 

 

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