Unsupervised Machine Learning Algorithms

In unsupervised machine learning, the training data is not labelled and here you have to find clusters to detect the similarities between different data points. In this article, I’m going to introduce you to all the unsupervised machine learning algorithms that you should know as a data science professional.

Unsupervised Machine Learning Algorithms

Below are some of the most important unsupervised machine learning algorithms that you should know as a data scientist:

  1. K-Means
  2. DBSCAN
  3. One-Class SVM
  4. Isolation Forest
  5. Principal Component Analysis
  6. Apriori

Now let’s go through all the unsupervised machine learning algorithms mentioned above one by one.

K-Means Clustering:

The K-means clustering algorithm is a very powerful unsupervised learning algorithm that is capable of grouping unlabeled data into a few iterations. The computational complexity of this algorithm is directly linear to the number of instances, the number of clusters and the number of dimensions. It is generally the fastest clustering algorithm.

DBSCAN Clustering:

DBSCAN clustering is a very simple and powerful clustering algorithm capable of identifying any number of clusters in any amount of data. Its computational complexity is very close to directly linear to the number of instances.

One-Class SVM:

One-class SVM is used for the novelty detection task. Here we only have one class of instances, so this algorithm tries to separate high dimensional space instances from the origin. This way, it can easily find the smallest regions that encompass all instances.

Isolation Forest:

The isolation forest algorithm is ideal for the anomaly detection task. It builds a random forest in which each decision tree is developed at random where it selects each feature at random. Then it divides the data into two halves. The data is thus cut into pieces until all the instances are isolated from the other instances.

Principal Component Analysis:

The principal component analysis is the most popular dimensionality reduction algorithm. It works by first identifying the hyperplane closest to the data, and then projecting the data onto it. This is how the principal component analysis works.

Apriori:

In Machine Learning, the Apriori algorithm is used for data mining association rules. Association mining is typically performed on transaction data from a retail marketplace or online e-commerce store. Since most transaction data is large, the apriori algorithm makes it easy to find these patterns or rules quickly.

Summary

So these were some of the most important unsupervised machine learning algorithms that you should know as a data scientist. I hope you liked this article on unsupervised machine learning algorithms. Feel free to ask your valuable questions in the comments section below.

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Aman Kharwal
Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder
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