Unsupervised Machine Learning

Unsupervised Machine Learning

Introduction

In the last decade, we have seen significant progress in Artificial Intelligence (AI) and  Machine Learning with respect to technology with supervised and reinforcement learning – in everything from self-driving cars to photo recognition.

Unsupervised learning, also known as unsupervised machine learning, uses Machine Learning Algorithms to cluster and analyze unlabelled datasets. The algorithms identify hidden data groupings or patterns without the need for any human intervention. Its capability to identify differences and similarities in information makes it the ideal solution for exploratory cross-selling strategies, image recognition, data analysis, and customer segmentation.

Why Unsupervised Learning?

Following are the main reasons to use Unsupervised Learning in Machine Learning:

• Identifies all kinds of hidden patterns in data.
• Helps you to identify features that can be useful for categorization.
• It takes real-time, thus, the analysis and labelling of data take place in the presence of learners.
• It is much easier to get unlabelled data from a computer system, as it does not need manual intervention.

Some applications of unsupervised learning include:

1. Clustering enables the division of the dataset into groups according to resemblance. However, very often the cluster analysis overestimates the similarity between the groups and does not treat data points as individuals. It is for this reason that cluster analysis is not a great choice for applications such as targeting and customer segmentation.
2. Anomaly detection can identify unusual data points in your dataset automatically. This is very useful in pinpointing fake transactions, discovering defective pieces of hardware, or recognizing an outlier that is a result of a human error during data entry.
3. Association mining detects sets of items that often occur together in the dataset. Retailers use association mining for basket analysis, as it enables analysts to identify goods that customers purchase at the same time and create more merchandising strategies and effective marketing.
4. Latent variable models are usually used for data preprocessing, such as decreasing the features in a dataset (dimensionality reduction) or decomposing the dataset into various components.

Discriminative and Generative Models of Unsupervised Learning

The patterns you identify with unsupervised machine learning methods can also help implement supervised machine learning methods in the future. For example, one may use an unsupervised technique to perform cluster analysis on the data and then use the cluster to which each row belongs as an additional feature in the supervised learning model. Another example is a fraud recognition model that uses anomaly detection scores as an additional feature.

There are two types of Unsupervised Learning: discriminative models and generative models. Discriminative models offer information such as, if you provide X then the consequence of the same is Y. However, the generative model lets you know the entire probability that you are going to see X and Y at the same time.

Active Learning is the Future

Active learning or semi-supervised learning takes the best of both supervised and unsupervised learning.  It puts them together to make predictions on how a network should behave. It starts with unsupervised learning by looking for any patterns on a network that deviate from the norm. Once the patterns are identified, then one can label them as a threat, which is the supervised learning portion.

An active learning platform is very useful as it continuously scans for deviations on the network. It also constantly labels and adds metadata to the abnormalities it finds. This makes it a very strong identification and response system.