Get Started
Email hello@westlink.com Phone (866) 954-6533
(Opens in a new tab) LinkedIn
Blog / Machine Learning, Machine Learning Explained / Unsupervised Learning: Machine Learning Explained

Unsupervised Learning: Machine Learning Explained

Jan. 29, 2024
10 min
Nathan Robinson
Nathan Robinson
Product Owner
Nathan is a product leader with proven success in defining and building B2B, B2C, and B2B2C mobile, web, and wearable products. These products are used by millions and available in numerous languages and countries. Following his time at IBM Watson, he 's focused on developing products that leverage artificial intelligence and machine learning, earning accolades such as Forbes' Tech to Watch and TechCrunch's Top AI Products.

In the realm of machine learning, unsupervised learning stands as a significant pillar, alongside supervised and reinforcement learning. This method of machine learning involves the use of machine learning algorithms to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.

Unsupervised learning is incredibly important in the field of machine learning. It’s used to model the underlying structure or distribution in the data in order to learn more about the data. These models are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Rather than relying on present data like supervised machine learning, algorithms are left to their own devises to discover and present the interesting structure in the data.

Types of Unsupervised Learning

Unsupervised learning can be divided into two types of problems, namely: clustering and association. Clustering involves grouping a set of objects in such a way that objects in the same group (a cluster) are more similar to each other than to those in other groups. On the other hand, association is a rule-based machine learning method where an idea is extracted based on a particular relationship between various items in the dataset.

Each of these types of unsupervised learning plays a crucial role in the analysis of complex data, enabling the extraction of patterns, relationships, and structures that may not be immediately apparent. They are particularly useful in scenarios where manually labeling data is impractical or impossible.

Clustering

Clustering is a machine learning technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. In theory, data points that are in the same group should have similar properties and/or features, while data points in different groups should have highly dissimilar properties and/or features.

Clustering is a method of unsupervised learning and a common technique for statistical data analysis used in many fields. In Data Mining, K-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster.

Association

Association rules analysis is a technique to uncover how items are associated to each other. There are three common ways to measure association: support, confidence and lift.

Measure 1: Support

This says how popular an itemset is, as measured by the proportion of transactions in which an itemset appears.

Measure 2: Confidence

This says how likely item Y is purchased when item X is purchased, expressed as {X -> Y}. This measure is based on the proportion of transactions with item X, in which item Y also appears.

Measure 3: Lift

This says how likely item Y is purchased when item X is purchased, while controlling for how popular item Y is. In the first step of Apriori algorithm, we build the item frequency dataset for all the items in the original dataset and select the important (frequent) items.

Applications of Unsupervised Learning

Unsupervised learning has numerous applications spanning various domains. Some of the most common applications include anomaly detection, which is commonly used for fraud detection or detecting defects in manufacturing; visualization, which is used to understand and interpret complex datasets by reducing them to two or three dimensions; and dimensionality reduction, which is used to simplify high-dimensional datasets while retaining their essential features.

Another key application of unsupervised learning is in the field of Natural Language Processing (NLP), where techniques such as topic modeling can be used to discover the main topics in a large corpus of text. Similarly, unsupervised learning can be used in social network analysis to identify communities or clusters within the network.

Anomaly Detection

Anomaly detection is an important application of unsupervised learning where the objective is to identify unusual data points or observations in the dataset. These anomalies often translate to critical and actionable information in several industries. For instance, credit card companies can detect fraudulent transactions, while manufacturing companies can detect abnormal machine behavior to prevent failures.

In the context of machine learning, anomaly detection can be considered as a classification task where a model is trained to predict whether a given data point is normal (non-anomalous) or abnormal (anomalous). However, unlike typical classification tasks, anomaly detection often deals with highly imbalanced datasets as anomalies are typically rare or infrequent occurrences.

Visualization

Visualization is a powerful application of unsupervised learning that is used to understand and interpret complex datasets. By reducing high-dimensional data to two or three dimensions, visualization techniques allow us to visually inspect the structure and patterns in the data. This can be particularly useful in exploratory data analysis, where we are interested in understanding the underlying structure of the data.

Common techniques for visualization include Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). PCA is a linear dimensionality reduction technique that can be used for extracting information from high-dimensional spaces by projecting it into a lower-dimensional subspace. t-SNE, on the other hand, is a non-linear technique that is particularly well-suited for the visualization of high-dimensional datasets.

Challenges in Unsupervised Learning

While unsupervised learning offers considerable potential in identifying patterns and anomalies, it faces distinct and complex challenges.

Quality Assessment

Assessing the quality of the results produced by unsupervised learning algorithms is a significant challenge. This is because, unlike in supervised learning, we do not have a clear ground truth to compare against. As a result, it can be difficult to determine whether the patterns and relationships uncovered by the algorithm are meaningful or simply a result of noise or randomness in the data.

One common approach to address this challenge is to use internal evaluation metrics that measure the quality of the clustering results without the need for external labels. Examples of such metrics include the silhouette score and the Davies-Bouldin index. However, these metrics have their limitations and should be used with caution.

Computational Cost

Unsupervised learning algorithms often involve complex optimization problems that can be computationally expensive. This is particularly true for methods like hierarchical clustering, which can have a computational complexity of O(n3), where n is the number of data points. As a result, these methods can be impractical for large datasets.

One approach to address this challenge is to use more efficient algorithms, such as k-means or DBSCAN, which have a computational complexity of O(n). Another approach is to use dimensionality reduction techniques to reduce the size of the dataset before applying the clustering algorithm. However, these approaches come with their own trade-offs and should be chosen based on the specific requirements of the task at hand.

Conclusion

Unsupervised learning represents a key category of machine learning algorithms. It offers significant advantages in situations where labeled data is scarce or unavailable and can uncover hidden patterns and relationships in the data that may not be apparent through supervised learning techniques. Despite the issues with unsupervised learning, such as quality assessment and computational cost, it remains a powerful tool in the data scientist’s toolkit.

As we continue to generate more and more data, the importance of unsupervised learning is likely to increase. Future research in this field will likely focus on developing more efficient algorithms, as well as better methods for assessing the quality of the results. With these advancements, unsupervised learning will continue to play a crucial role in our ability to understand and make sense of the world around us.

Nathan Robinson
Nathan Robinson
Product Owner
Nathan is a product leader with proven success in defining and building B2B, B2C, and B2B2C mobile, web, and wearable products. These products are used by millions and available in numerous languages and countries. Following his time at IBM Watson, he 's focused on developing products that leverage artificial intelligence and machine learning, earning accolades such as Forbes' Tech to Watch and TechCrunch's Top AI Products.

Comments

Subscribe
Notify of
guest
0 Comments
Inline Feedbacks
View all comments