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Introduction

Many cases in which we do not have labeled data and need to find the hidden patterns from the given dataset. Unsupervised Learning is a machine learning technique in which models are not supervised using training dataset. Models itself find the hidden patterns and insights from the given data. It can be compared to learning which takes place in the human brain while learning new things. 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.

The goal is to find the underlying structure of dataset, group that data according to similarities, and represent that dataset in a compressed format.

Types of Unsupervised Learning:

1. Clustering: Clustering is a method of grouping the objects into clusters such that objects with most similarities remain into a group and objects that have less or no similarities remain within another group. Cluster analysis finds the commonalities between the data objects and categorizes them as per the presence and absence of those commonalities.

2. 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) are also tend to purchase Y (Butter/Jam) item. A typical example of Association rule is Market Basket Analysis.

Unsupervised Learning Algorithms

1. K-means Clustering

2. KNN (K-Nearest Neighbors)

3. Hierarchical Clustering

4. Anomaly Detection

5. Neural Networks

6. Principle Component Analysis

7. Independent Component Analysis

8. Apriori Algorithm

9. Singular Value Decomposition

Clustering

Clustering is a machine learning technique, which groups an unlabeled dataset. It is done by finding some similar patterns in the unlabeled dataset such as shape, size, color, behavior, etc., and divides them as per the presence and absence of those similar patterns.

Clustering Methods

• Hard clustering is when a data point belongs to only one group.

• Soft Clustering is when data points can belong to another group as well.

Different Clustering Methods

1. Partitioning Clustering

2. Density-based Clustering

3. Distribution Model-based Clustering

4. Hierarchical Clustering

5. Fuzzy Clustering