What is Clustering?
An introduction to Machine learning algorithms
The objective of clustering is to divide the population or data points into a number of groups such that the data points in the same groups are more similar to one another and different from the data points in other groups. In this article, I will define clustering and explain the many types of clustering.
What are clustering algorithms?
The process of grouping objects based on similarities is called clustering. Described as an unsupervised learning problem with the objective of producing training data using a specific set of inputs but without any predetermined goal values. In order to make a collection of unlabeled data more comprehensible and manipulable, it is the process of looking for comparable structural features. A cluster is a group of data points that are connected to one another through their connections to nearby data points. Two uses for clustering are pattern analysis and feature engineering.
Types of clustering algorithms
Different clustering techniques exist that can handle various sorts of data.
Density-based
Data is organised into clusters that have high data point densities surrounded by low data point densities. In essence, the algorithm identifies areas with a high density of data points and designates those areas as clusters.
Distribution-based
Based on the likelihood that each data point belongs to a certain cluster, all of the data points are regarded as components of that cluster.
Centroid-based
These methods divide data points depending on several centroids present in the data. In accordance with its squared distance from the centroid, each data point is grouped into a cluster. The most popular kind of clustering is this one.
Hierarchical-based
Everything is arranged top-down by creating a tree of clusters.
Popular clustering algorithms
- K-means clustering algorithm
- Gaussian Mixture Model algorithm
- DBSCAN clustering algorithm
- BIRCH algorithm
- Mean-Shift clustering algorithm
- OPTICS algorithm
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Conclusion
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