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Clustering algorithms are a powerful technique for machine learning on unsupervised data. The most common algorithms in machine learning are hierarchical clustering and K-Means clustering. These two algorithms are incredibly powerful when applied to different machine learning problems.
Both k-means and hierarchical clustering have been applied to different scenarios to help gain new insights into the problem. Before diving into the innovative uses of clustering algorithms, I will first share an overview of the two algorithms. 

What is unsupervised learning?

Before we get started, let me first introduce the concept of unsupervised learning. Unsupervised learning is where you train a machine learning algorithm, but you don’t give it the answer to the problem.

1) K-means clustering algorithm

The K-Means clustering algorithm is an iterative process where you are trying to minimize the distance of the data point from the average data point in the cluster.

2) Hierarchical clustering

Hierarchical clustering algorithms seek to create a hierarchy of clustered data points.
The algorithm aims to minimize the number of clusters by merging those closest to one another using a distance measurement such as Euclidean distance for numeric clusters or Hamming distance for text.

Here are 7 examples of clustering algorithms in action.

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