Clustering is a type of Unsupervised Machine Learning Technique. Unsupervised Machine Learning is a type of Machine Learning where output is not known. The data set is not labeled. The unsupervised machine Learning Algorithms have to self-discover the pattern in the data set.
The algorithms like Clustering, Neural Networks, Anomaly detection are used to find the patterns in the data, to help analyze the data further.
In this blog let us understand Clustering and types of clustering
Clustering
In Clustering the algorithm automatically groups the data points into categories with similar features, Clustering Algorithms separate the data into sub-sets, which help in understanding and analyzing the data.
Clustering is a machine learning technique where similar data are grouped into a single cluster.
Clustering makes it easier to identify similar data.
Types of Clustering
- Centroid-based clustering– This clustering organizes the data into non-hierarchical clusters example is K-means.entroid-based algorithms are efficient but sensitive to initial conditions and outliers.
- Density-based clustering -This clustering connects areas of high example density into clusters. This allows for arbitrary-shaped distributions as long as dense areas can be connected.
- Distribution-based Clustering– The clustering assumes data is composed of distributions ,example is Gaussian Distribution
- Hierarchical Clustering– creates a tree of clusters, example- taxonomies
