Unsupervised Machine Learning Algorithms and Applications

In the last article, we came across the criteria on which we can broadly classify Machine Learning Algorithms. We also came across the various types of Machine Learning algorithms and their working in brief. In this article, we will cover one of the most important concepts of Machine Learning algorithms – Unsupervised Machine Learning. As you have noticed that this type of learning does not have much application. However, it is amongst one of the important concepts of ML. Let’s learn more.

What is Unsupervised Machine Learning?

Unsupervised learning is a machine learning technique in which we do not supervise the training of the models using a training or labeled dataset. As a replacement for that, we train the models in such a way that they themselves recognize the hidden patterns and insights from the given data. We can compare this technique to the biological learning techniques of the human brain.

We can define Unsupervised learning as a type of machine learning in which we train the models using an unlabeled dataset and we do not allow them to operate on that data without any supervision of the trained data.

However, we cannot directly apply Unsupervised learning to a regression or classification problem. This anomaly occurs because, unlike supervised learning, we have the input data but no corresponding output data to map the input.

Unsupervised learning mainly focuses on finding the underlying structure of the dataset, the grouping that data according to similarities, and representing that dataset in a compressed format for ease in regression and classification problems.

Why Use Unsupervised Machine Learning?

There are a variety of reasons for which we should use Unsupervised Machine Learning techniques for numerous problems. We have stated below some of such reasons:

1. It is beneficial in finding useful insights from the given input data.

2. It works in the same fashion as a human learns to think by their own experiences, which meets the real purpose of Artificial Intelligence.

3. We train the Unsupervised learning models using unlabeled data and it works on the same. This work on uncategorized data makes unsupervised learning more important.

4. The real-world datasets mainly comprise data that is unstructured and that does not have any pre-mapping with the output. In order to solve such cases, we need unsupervised learning.

Working of Unsupervised Machine Learning

In an attempt to understand the brief working of Unsupervised ML, we consider unlabeled input data, which means we have not pre-categorized the data and also haven’t mapped it to the corresponding outputs. As the first step, we feed this unlabeled input data to the machine learning model in order to train it.

Further, the model will interpret the raw data to recognize the hidden patterns from the data. After finding some relationship between the data points, the model then will apply suitable algorithms such as k-means clustering, Decision tree, and so on in order to classify the raw data.

Once it applies the preferable algorithm, the algorithm classifies the data objects into groups on the basis of the similarities and differences between the objects.

Types of Unsupervised Machine Learning

We can further divide the Unsupervised Machine Learning algorithm into two types on the basis of the problems that we need to tackle.

1. Clustering

Clustering is a technique of grouping the objects into clusters in such a way that the objects with the most similarities remain in a group. Apart from that, the objects having less or no similarities with other objects of another group. Cluster analysis discovers the commonalities between the data objects and on the basis of these commonalities, categorizes them into various groups.

2. Association

An association rule is an unsupervised learning technique that we make use of for finding the relationships between variables in large databases. It tends to determine the set of items that occur collectively in the dataset. Association rule is beneficial for making marketing strategy more effective. As an example, consider if people who buy X items also tend to purchase Y items. A typical real-life application of the Association rule is Market Basket Analysis.

Learning Algorithms for Unsupervised Machine Learning

We have enlisted below, some of the major algorithms that follow unsupervised learning:

1. K-means clustering

K-means algorithm is a clustering algorithm type. It follows an iterative clustering approach for clustering the given input dataset. This algorithm follows the principle that similar data points should be in close proximity to each other as compared to dissimilar points.

2. KNN (k-nearest neighbors)

KNN or more commonly known as K-nearest neighbor is another type of clustering-based algorithm. We make use of this method for those data points which we can select in any class or for those who don’t have any class or cluster assigned to them.

3. Hierarchical clustering

This is another type of clustering algorithm, in which we form multiple clusters, which are distinct from each other, however, the contents inside the cluster are highly analogous to each other. In order to achieve so, we would make use of the distance matrix for calculation purposes. As the next step, for the visual representation of the clusters, the algorithm will form a dendrogram.

There are 4 types of Hierarchical Classifications: Ward’s Linkage(states that the distance between two clusters is defined by the increase in the sum of squared), Average Linkage (defined by the mean distance between two points in each cluster), Complete Linkage(maximum linkage defined by the maximum distance between two points in each cluster), Single Linkage(minimum linkage defined by the minimum distance between two points in each cluster)

4. Anomaly detection

We use this type of unsupervised ML method to look out the occurrences of rare events or observations that do not occur in normal instances. By making use of the learned knowledge, anomaly detection methods are able to differentiate between anomalous or a normal data point according to its training.

5. Principal Component Analysis

Principal component analysis (PCA) is a type of dimensionality reduction algorithm, which we use to reduce redundancies of the larger datasets in order to compress datasets through feature extraction.

6. Apriori algorithm

Apriori algorithms have become popular through market basket analysis. This technique leads to different recommendation engines for music platforms and online retailers to fulfill customer demands. We use them within transactional datasets to recognize frequent item-sets, or collections of items, to ascertain the likelihood of consuming a product given the consumption of another product beforehand.

7. Singular value decomposition

Singular value decomposition (SVD) is yet another type of dimensionality reduction approach which factorizes a given input matrix, A, into three, low-rank matrices. We can represent SVD through the representation, A = USVT, where U and V are orthogonal matrices.

8. Probabilistic Clustering

A probabilistic model is an unsupervised technique that assists us to solve density estimation or soft clustering problems. In probabilistic clustering, the algorithm clusters data points based on the likelihood that they belong to a particular distribution. The Gaussian Mixture Model (GMM) is one of the most commonly used probabilistic clustering methods.

9. Association Rules

An association rule is a rule-based method for detecting relationships between variables in a given dataset. These methods are often used for market basket analysis, allowing companies to better understand relationships between different products. Realizing the consumption habits of customers enables businesses to develop better cross-selling strategies and recommendation engines.

10. Dimensionality Reduction

While more data effectively yields more accurate results, it can also impact the performance of machine learning algorithms (like causing overfitting) and it can also make it even more difficult to visualize datasets. Dimensionality reduction is a technique that we can make use of when the number of features, or dimensions, in a given dataset is too high.

11. Autoencoders

Autoencoders tend to leverage neural networks to compress data and then recreate a new representation of the original input of data. You can understand this by considering the example of working of neural network structure, where you can see that the hidden layer specifically acts as a bottleneck to compress the input layer prior to reconstructing within the output layer.

Advantages of Unsupervised Machine Learning

1. Labeling of data accounts for a lot of manual work and expenses. Unsupervised learning solves this problem by processing the data and classifying it without any requirements of labels.

2. We can add the labels later on after the algorithm classifies the data, which is much easier.

3. It proves to be very helpful in finding patterns in the raw data, which are not possible to find using conventional methods.

4. We can easily accomplish Dimensionality reduction using unsupervised learning.

5. This proves to be the perfect tool for data scientists, as unsupervised learning can help to understand raw data with much precision.

6. We can also find out the degree to which the data is similar. The algorithm achieves this can with probabilistic methods.

7. This type of learning replicates human intelligence to some extent as the model learns slowly and then predicts the result.

Disadvantages of Unsupervised Machine Learning

1. The result might lack accuracy as we do not have any input data to train the dataset for accuracy.

2. The model is learning from raw data without any prior knowledge leading to mismatched results.

3. It is also a resource-consuming process. The learning phase of the algorithm might take a lot of time, as it analyses and calculates all possibilities on the raw data.

4. For some projects that involve the use of live data, it might require continuous feeding of data to the model, which will result in both inaccurate and time-consuming results for the algorithm.

5. The more the features, the more the complexity increases for the prediction of the results.

Applications of Unsupervised Machine Learning

1. Market Basket Analysis

It is a machine learning model that follows the principle that if you buy a certain group of items, you are less or more likely to buy another group of items along with the first set.

2. News Sections

Google News makes use of unsupervised learning to categorize articles on the same story from various online news outlets. As an example, consider the results of a presidential election that we could categorize under their label for “US” news.

3. Computer Vision

We can make use of Unsupervised learning algorithms for visual perception tasks, such as object recognition.

4. Medical Imaging

Unsupervised machine learning facilitates essential features to medical imaging devices, such as image detection, classification, and segmentation.

5. Customer Persona

By defining customer personas, we can make it easier to understand common traits and business clients’ purchasing habits. Unsupervised learning enables businesses to build better buyer persona profiles, allowing organizations to align their product messaging more accurately.

6. Recommendation System

By making use of past purchase behavior data, unsupervised learning can assist us to discover data trends that we can use to develop more effective cross-selling strategies.

7. Semantic Clustering

As we might have observed, semantically similar words share a similar context. People post their queries on websites in their own ways consisting of combinational words and phrases. Semantic clustering aims to group all these responses having the same meaning in a cluster. The algorithm does this in order to ensure that the customer finds the information they want quickly and easily. It plays a crucial role in information retrieval, good browsing experience, and other comprehension-related problems.

8. Delivery Store Optimization

We extensively make use of Machine learning models to predict the demand and keep up with supply. We can even use them to open stores where the demand is higher and optimize roots for more efficient deliveries according to past data and behavior.

Conclusion

With that, we have reached the end of this article that talked in brief about the basics of Unsupervised Learning in Machine Learning. Through this article, we realized that even though Unsupervised Learning does not have much real-life application, it still plays a pivotal role in the arena of Machine Learning. We came to know about the advantages, disadvantages, as well as applications of Unsupervised Learning. Hope that this article from PythonGeeks was able to solve all your queries related to the concept of Unsupervised Learning.