https://www.javatpoint.com/unsupervised-machine-learning
Unsupervised learning is a type of machine learning in which models are trained using unlabeled dataset and are allowed to act on that data without any supervision.
Why use Unsupervised Learning? Below are some main reasons which describe the importance of Unsupervised Learning:
Unsupervised learning is helpful for finding useful insights from the data. Unsupervised learning is much similar as a human learns to think by their own experiences, which makes it closer to the real AI. Unsupervised learning works on unlabeled and uncategorized data which make unsupervised learning more important. In real-world, we do not always have input data with the corresponding output so to solve such cases, we need unsupervised learning.
Clustering: Clustering is a method of grouping the objects into clusters such that objects with most similarities remains into a group and has less or no similarities with the objects of another group. Cluster analysis finds the commonalities between the data objects and categorizes them as per the presence and absence of those commonalities.
clustering or cluster analysis is an unsupervised learning problem. It is often used as a data analysis technique for discovering interesting patterns in data, such as groups of customers based on their behavior.
K-Means
https://www.javatpoint.com/k-means-clustering-algorithm-in-machine-learning
The algorithm takes the unlabeled dataset as input, divides the dataset into k-number of clusters, and repeats the process until it does not find the best clusters. The value of k should be predetermined in this algorithm.
The k-means clustering algorithm mainly performs two tasks:
Determines the best value for K center points or centroids by an iterative process. Assigns each data point to its closest k-center. Those data points which are near to the particular k-center, create a cluster. Hence each cluster has datapoints with some commonalities, and it is away from other clusters.