The unsupervised learning algorithm can be further categorized into two types of problems:

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.

Association

An association rule is an unsupervised learning method which is used for finding the relationships between variables in the large database. It determines the set of items that occurs together in the dataset.

Association rule makes marketing strategy more effective.

Such as people who buy X item (suppose a bread) also tend to purchase Y (Butter/Jam) item. A typical example of Association rule is Market Basket Analysis.

Unsupervised Learning algorithms:

K-means clustering

KNN (k-nearest neighbors)

Hierarchal clustering

Anomaly detection

Neural Networks

Principle Component Analysis

Independent Component Analysis

Apriori algorithm

Singular value decomposition

Advantages of Unsupervised Learning

Fast process as task of data labelling is not involved.

Provides unique and disruptive insights.

Disadvantages of Unsupervised Learning

The result of the unsupervised learning algorithm might be less accurate as input data is not labelled, and algorithms do not know the exact output in advance.

Difficult to measure accuracy

Dealing with high-dimensional data- when the dimension of data and the number of variables is more, the process becomes difficult.