A Guide to Unsupervised Machine Learning Models and Types

Summary: Unsupervised Machine Learning doesn’t rely on labelled data, making it suitable for scenarios with unlabelled or abundant data. It discovers patterns autonomously, aiding in complex tasks like clustering and anomaly detection. Its applications span diverse fields, offering insights, scalability, and flexibility in data analysis.

Introduction

Machine Learning is a subset of artificial intelligence (AI) that focuses on developing models and algorithms that train machines to think and work like humans. It allows computers to acquire knowledge and make predictions or decisions without being specifically programmed. It entails developing computer programs that can improve themselves independently based on expertise or data. 

There are two types of techniques: supervised and unsupervised learning. The following blog will focus on Unsupervised Machine Learning models, describing the algorithms and types with examples.

What is Unsupervised Machine Learning?

In unsupervised Learning, the model operates without user supervision. Significantly, the method allows the model to work independently by discovering its patterns and previously undetected information. Therefore, it mainly deals with unlabelled data. 

Unsupervised learning’s ability to discover similarities and differences in data makes it ideal for exploratory data analysis. It is also helpful in enabling cross-selling strategies, customer segmentation, and image recognition.

Unsupervised Machine Learning Algorithms tend to perform more complex processing tasks than supervised learning. However, it can be highly unpredictable compared to natural learning methods. Different unsupervised learning algorithms exist, including clustering, anomaly detection, neural networks, etc.

Example

Taking an unsupervised Machine Learning models example, let’s consider a dataset that contains images of different cats and dogs. The algorithm was never trained based on the given dataset, meaning it has no idea about it. 

The unsupervised learning algorithm’s task is to identify the features of the image on its own. The algorithm performs this task using unsupervised learning clustering, dividing the dataset into groups based on image similarities.

Reasons for Using Unsupervised Learning Algorithms

There are several reasons for using unsupervised algorithms in Machine Learning models. First, it finds all kinds of unknown patterns within a particular dataset. Second, unsupervised learning helps find features that are helpful for categorisation.

The third reason is that learning takes place in real time, which implies that all the input data must be analysed and labelled in the presence of the users. The fourth and last reason is that acquiring unlabelled data from computer systems is more accessible than labelled data.

Types of Unsupervised Learning Algorithms

Understanding these various unsupervised learning algorithms enables better data analysis, pattern recognition, and clustering. It enhances insights into complex datasets, aids anomaly detection, and fosters innovation in Machine Learning applications.  There are two main types: clustering and association. Let’s examine these categories in more detail.

Clustering

Cluster analysis is a method of grouping objects into clusters, focusing on which it is possible to find the similarities between a group and another that does not have any similarities. It helps find similarities between data objects and categorise them, emphasising the presence or absence of commonalities.

Association

The association rule in unsupervised Machine Learning models includes the method for finding the relationship between variables within large volumes of datasets. The technique helps determine the set of items within a dataset. With the help of this method, companies will find marketing strategies more effective. Market-based analysis can be considered a typical example of an Association rule.

Different Types of Clustering In Unsupervised Learning Algorithms

Understanding different types of clustering enables tailored data analysis, pattern recognition, and segmentation, which is crucial for diverse fields like customer segmentation, anomaly detection, and image classification, enhancing decision-making and insights. 

Considering that Learning Clustering is a crucial part of the datasets, following are the clustering types which has been explained as follows:

Hierarchical Clustering

Hierarchical clustering builds a hierarchy of clusters. It can be either agglomerative or divisive. In agglomerative clustering, each data point is a separate cluster. Then, it merges pairs of clusters based on their similarity until forming a single cluster. Divisive clustering starts with all data points in one cluster. It splits them recursively until each data point is in its cluster.

K-Means Clustering

K-means is a popular and widely used clustering algorithm. It aims to partition a given dataset into K clusters, where each data point belongs to the cluster with the nearest mean. It works iteratively by updating cluster centres and reassigning data points until convergence.

K-NN (K Nearest Neighbours)

K-Nearest Neighbors (K-NN) is a simple yet powerful algorithm for classification and regression tasks in Machine Learning. It is an instance-based or lazy learning algorithm that does not require explicit training. Instead, it uses the available labelled data to make predictions based on the proximity of data points in the feature space.

Principal Component Analysis

Principal Component Analysis (PCA) is a widely used dimensionality reduction technique in Machine Learning and data analysis. It aims to transform a high-dimensional dataset into a lower-dimensional space while preserving the most critical information and minimising variance loss.

Singular Value Decomposition

Singular Value Decomposition (SVD) is a matrix factorisation technique that decomposes a matrix into three separate matrices. This technique allows us to extract valuable insights and reduce the dimensionality of the data. SVD is widely used in various domains, including data analysis, image processing, natural language processing, and recommendation systems.

Independent Component Analysis

Independent Component Analysis (ICA) is a statistical technique for separating mixed signals into their source components. It is a powerful method for blind source separation, which aims to recover the source signals without knowing the mixing process.

Association

The association is the unsupervised learning algorithm which allows you to establish an association with the data objects within a set of large databases. This technique discovers the various exciting relationships between the variables in large databases. For instance, people buying a new home would most likely purchase new furniture.

Differences Between Supervised vs Unsupervised Machine Learning

Unsupervised Machine Learning Applications

Understanding various applications of these algorithms is crucial for unlocking insights from unstructured data, enhancing pattern recognition, improving clustering techniques, and advancing anomaly detection in diverse fields. 

News Sections: Google News employs this to categorise articles on the same news story from different online news outlets. For instance, the outcome of a presidential election could be classified under the “US” news label.

Computer Vision: In computer vision, people utilise this algorithm for visual perception tasks, like object recognition.

Medical Imaging: it plays a crucial role in medical imaging, aiding in tasks such as image detection, classification, and segmentation. These techniques are used in radiology and pathology to quickly and accurately diagnose patients.

Anomaly Detection: This learning models excel at scanning vast amounts of data and identifying unusual data points within a dataset. These anomalies can illuminate faulty equipment, human errors, or security breaches.

Customer Personas: Defining customer personas helps understand business clients’ common traits and purchasing habits. It enables businesses to create more refined buyer persona profiles, allowing organisations to align their product messaging more effectively.

Recommendation Engines: By analysing past purchase behaviour data, unsupervised learning can uncover data trends that data professionals can use to develop more successful cross-selling strategies. Retailers use this information to provide relevant add-on recommendations to customers during online retail checkout.

Advantages of Unsupervised Learning

Unsupervised learning facilitates handling intricate tasks that surpass the complexity of those addressed by supervised learning. The absence of labelled input data in unsupervised learning liberates it from the constraints of predefined categories or classes, enabling the exploration of more nuanced patterns and relationships within the data. 

Moreover, the accessibility of unlabeled data significantly simplifies the data acquisition process. In many real-world scenarios, obtaining labelled data can be arduous and expensive. In contrast, unlabeled data is often abundant and readily available. This abundance fosters the scalability of learning approaches, allowing for the analysis of vast datasets without the overhead of manual labelling.

Disadvantages of Unsupervised Learning

In contrast to supervised learning, unsupervised learning faces inherent challenges due to the absence of corresponding output labels. Unsupervised algorithms must autonomously discern meaningful patterns and structures within the data without explicit guidance on the desired outcomes, rendering the learning process inherently more complex and uncertain.

Consequently, the outcomes produced by unsupervised learning algorithms may exhibit lower accuracy compared to their supervised counterparts. The lack of labelled data deprives algorithms of crucial contextual information, leading to potential inaccuracies or misinterpretations in the inferred patterns or clusters. 

Consequently, the reliability and interpretability of the results generated by this algorithm may be compromised, necessitating careful validation and refinement processes.

Frequently Asked Questions

What are the advantages of Unsupervised Machine Learning?

Unsupervised ML tackles complex tasks, utilising unlabelled data to make data acquisition easier. It’s scalable due to abundant unlabeled data, making it ideal for large datasets without manual labelling overhead.

How does Unsupervised Machine Learning differ from Supervised Learning?

Unsupervised ML doesn’t rely on labelled data for training, whereas supervised learning does. Unsupervised learning uncovers patterns and relationships autonomously, making it suitable for scenarios where labelling is time-consuming or unavailable.

What applications benefit from Unsupervised Machine Learning?

Unsupervised ML is crucial in various fields, such as news categorisation, computer vision, medical imaging, anomaly detection, and customer persona definition. It enhances pattern recognition, clustering, and anomaly detection in diverse domains.

Conclusion

Unsupervised learning is a Machine Learning technique that does not require labelled data for training. Instead, it focuses on finding patterns, structures, and relationships within the data. It makes it useful when dealing with large datasets or when labelling is time-consuming or unavailable. 

This algorithm can uncover unknown patterns and provide valuable insights, thus helping to train the ML models to perform more efficiently.