Unsupervised learning lets machines learn on their own.

This type of machine learning (ML) grants AI applications the ability to learn and find hidden patterns in large datasets without human supervision. Unsupervised learning is also crucial for achieving artificial general intelligence.

Labeling data is labor-intensive and time-consuming, and in many cases, impractical. That’s where unsupervised learning brings a big difference by granting AI applications the ability to learn without labels and supervision.

## What is unsupervised learning?

Unsupervised learning (UL) is a machine learning technique used to identify patterns in datasets containing unclassified and unlabeled data points. In this learning method, an AI system is given only the input data and no corresponding output data.

Some reasons why unsupervised learning is essential.

- Unlabeled data is in abundance.
- Labeling data is a tedious task requiring human labor. However, the very process can be ML-powered, making labeling easier for the humans involved.
- It’s useful for exploring unknown and raw data.
- It’s useful for performing pattern recognition in large datasets.

Unsupervised learning can be further divided into two categories: **parametric unsupervised learning** and **non-parametric unsupervised learning**.

## How unsupervised learning works

## Types of unsupervised learning

Unsupervised learning problems can be classified into clustering and association problems.

### Clustering

Depending on how they work, clustering can be categorized into four groups as follows:

**Exclusive clustering:**As the name suggests, exclusive clustering specifies that a data point or object can exist only in one cluster.**Hierarchical clustering:**Hierarchical tries to create a hierarchy of clusters. There are two types of hierarchical clustering:**agglomerative**and**divisive**. Agglomerative follows the bottom-up approach, initially treats each data point as an individual cluster, and the pairs of clusters are merged as they move up the hierarchy. Divisive is the very opposite of agglomerative. Every data point starts in a single cluster and gets split as they move down the hierarchy.**Overlapping clustering:**Overlapping allows a data point to be grouped in two or more clusters.**Probabilistic clustering:**Probabilistic uses probability distributions to create clusters. For example, “green socks,” “blue socks,” “green t-shirt,” and “blue t-shirt” can be either grouped into two categories “green” and “blue” or “socks” and “t-shirt”.

### Association

Market basket analysis and web usage mining are made possible with the association rule.

## Unsupervised learning algorithms

Both clustering and association rule learning is implemented with the help of algorithms.

Apriori algorithm, ECLAT algorithm, and Frequent pattern (FP) growth algorithm are some of the notable algorithms used to implement the association rule. Clustering is made possible by algorithms such as k-means clustering and principal component analysis (PCA).

### Apriori algorithm

Apriori algorithm is built for data mining. It’s useful for mining databases containing a large number of transactions, for example, a database containing the list of items bought by shoppers in a supermarket. It is used for identifying the harmful effects of drugs and in market basket analysis to find the set of items customers are more likely to buy together.

### ECLAT algorithm

Equivalence Class Clustering and bottom-up Lattice Traversal, or ECLAT for short, is a data mining algorithm used to achieve itemset mining and find frequent items.

Apriori algorithm uses horizontal data format and so needs to scan the database multiple times to identify frequent items. On the other hand, ECLAT follows a vertical approach and is generally faster as it needs to scan the database only once.

### Frequent pattern (FP) growth algorithm

The frequent pattern (FP) growth algorithm is an improved version of the Apriori algorithm. This algorithm represents the database in the form of a tree structure known as a frequent tree or pattern.

Such a frequent tree is used for mining the most frequent patterns. While the Apriori algorithm needs to scan the database n+1 times (where n is the length of the longest model), the FP-growth algorithm requires just two scans.

### K-means clustering

Many iterations of the k-means algorithm are widely used in the field of data science. Simply put, the k-means clustering algorithm groups similar items into clusters. The number of clusters is represented by k. So if the value of k is 3, there will be three clusters in total.

This clustering method divides the unlabeled dataset so that each data point belongs to only a single group with similar properties. The key is to find K centers called cluster centroids.

Each cluster will have one cluster centroid, and on seeing a new data point, the algorithm will determine the closest cluster to which the data point belongs based on metrics like the euclidean distance.

### Principal component analysis (PCA)

The principal component analysis (PCA) is a dimensionality-reduction method generally used to reduce the dimensionality of large datasets. It does this by converting a large number of variables into a smaller one that contains almost all the information in the large dataset.

Reducing the number of variables might affect the accuracy slightly, but it could be an acceptable tradeoff for simplicity. That’s because smaller datasets are easier to analyze, and machine learning algorithms don’t have to sweat much to derive valuable insights.

## Supervised vs. unsupervised learning

Here’s a quick look at the key differences between supervised and unsupervised learning.

Unsupervised learning | Supervised learning |

It’s a complex process, requires more computational resources, and is time-consuming. | It’s relatively simple and requires fewer computational resources. |

The training dataset is unlabeled. | The training dataset is labeled. |

Less accurate, but not necessarily | Highly accurate |

Divided into association and clustering | Divided into regression and classification |

It’s cumbersome to measure the accuracy of the model along with uncertainty. | It’s easier to measure the accuracy of the model. |

The number of classes is unknown. | The number of classes is known. |

Learning takes place in real-time. | Learning takes place offline. |

Apriori, ECLAT, k-means clustering, and Frequent pattern (FP) growth algorithm are some of the algorithms used. | Linear regression, logistic regression, Naive Bayes, and support vector machine (SVM) are some of the algorithms used. |

## Examples of unsupervised machine learning

Some real-world applications of unsupervised machine learning.

**Anomaly detection:**It’s a process of finding atypical data points in datasets and, therefore, useful for detecting fraudulent activities.- Computer vision: Also known as image recognition, this feat of identifying objects in images is essential for self-driving cars and even valuable for the healthcare industry for image segmentation.
**Recommendation systems:**By analyzing historical data, unsupervised learning algorithms recommend the products a customer is most likely to buy.**Customer persona:**Unsupervised learning can help businesses build accurate customer personas by analyzing data on purchase habits.

#### Leaving algorithms to their own devices

The ability to learn on its own makes unsupervised learning the fastest way to analyze massive volumes of data. Of course, choosing between supervised or unsupervised (or even semi-supervised) learning depends on the problem you’re trying to solve and the time and vastness of the data available. Nevertheless, unsupervised learning can make your entire effort more scalable.

The AI we have today isn’t capable of world domination, let alone disobeying its creators’ orders. But it makes incredible feats like self-driving cars and chatbots possible. It’s called narrow AI but isn’t as weak as it sounds.