By finding hidden patterns in training datasets, Unsupervised Learning (UL) rises as a key Machine Learning technique.
Further, we can see its applications in several daily-used digital products, like music apps and online stores!
Yet, what exactly is Unsupervised Learning? Let's unveil its definition and business possibilities.
What is Unsupervised Learning?
Unsupervised Learning is a type of Machine Learning where algorithms act as explorers venturing into uncharted data. Unlike approaches that rely on pre-labeled data, Unsupervised Learning algorithms analyze unlabeled data to uncover hidden patterns and structures.
By sifting through the data itself, these algorithms can group similar data points together, classify data into previously unknown categories, or even reduce complex datasets into a more manageable format.
This technique excels in various applications like anomaly detection, Internet of Things, Network Operations and Analytics, detection of fraud, and image recognition.
Attributing the invention of Unsupervised Learning to a single person is difficult because it evolved gradually from various research areas. However, some key figures made significant contributions to its development:
● Donald Hebb (1949): Proposed the Hebbian learning rule, a foundational concept in Neural Networks that underpins Unsupervised Learning algorithms like clustering.
● Geoffrey Hinton (1980s): Pioneered research in Boltzmann machines, a type of Unsupervised Neural Network used for dimensionality reduction and feature extraction.
● Teuvo Kohonen (1980s): Developed the Self-Organizing Map (SOM) algorithm, a popular Unsupervised Learning technique for Data Visualization and clustering.
● Stuart Geman and Donald Geman (1984): Introduced the Markov Random Field (MRF) model, which has applications in image segmentation and anomaly detection.
Types of Unsupervised Learning Algorithms
Hierarchical Clustering
Clustering algorithms group similar data points, pairing up the most similar ones and then grouping them into larger clusters, creating a tree-shaped structure or dendrogram. Such clusters at different levels reveal data relationships at multiple scales.
Imagine you’re building a product and want to understand your target customers, you might collect data from various demographics or user types through User Research.
Cluster Analysis can help identify distinct user groups with similar needs, behaviors, and pain points, allowing you to tailor your product features to serve each segment better.
Principal Component Analysis (PCA)
Principal Component Analysis (PCA) reduces data complexity, simplifying it without sacrificing key features. By identifying the most vital elements, it focuses on the core information, which subsequently facilitates visualization and improves Machine Learning algorithms’ efficiency.
For instance, if you’re evaluating a product based on factors like usability, aesthetics, and functionality. PCA can analyze these factors and identify the underlying trends, reducing them to a smaller set of key dimensions that simplifies the evaluation process and helps prioritize design decisions.
Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
Algorithms like DBSCAN define clusters as regions of high density. They handle oddly shaped data and identify noise or outliers, which is great for when the number of clusters isn't known beforehand.
These algorithms can help you uncover clusters representing users who frequently use specific features together when analyzing similarities in User Behavior. This way, you can tailor your design decisions about feature development for a more intuitive User Experience (UX).
Independent Component Analysis (ICA)
Independent Component Analysis separates a multivariate signal into additive, independent non-Gaussian signals. It’s especially valuable when you want to determine factors that influence the overall data structure.
In Product Development, ICA can analyze User Interaction data to identify independent user actions within a complex feature set. This study can reveal how users interact with features independently and how these interactions might influence their overall experience.
Association Rule Learning (ARL)
Ever notice how grocery stores place peanut butter next to jelly? Association Rule Learning algorithms uncover relationships between different data points.
It's like finding frequently occurring patterns and helping businesses understand customer behavior. You may have seen these in eCommerce platforms in the “people who bought this item also bought” section.
5 Common Unsupervised Learning Techniques
1. K-means Clustering
K-means methodically divides the data into distinct non-overlapping groups (or "clusters") based on similarity.
The "K" refers to the number of groups you want to divide your data into, and the algorithm finds the best groupings by minimizing the distance between data points and the center of their group.
It's commonly used in market segmentation, where companies classify customers with similar buying behaviors into groups for targeted marketing.
2. Association Rule Mining
Imagine observing shoppers and noting down items that often end up in the shopping cart together. Association rule mining does this at a much larger scale, but the key difference is it doesn't require pre-defined rules.
Unlike Association Rule Learning, which focuses on uncovering specific relationships based on pre-existing hypotheses, association rule mining uncovers these relationships automatically within vast datasets.
This technique is often applied in transactional data to understand customer purchasing patterns, hence the popular market basket analysis.
3. Non-negative Matrix Factorization
When someone gives five stars to a movie, you don't just learn about their preference for that one flick; you learn about their tastes in general.
Non-negative Matrix Factorization (NMF) extracts broader patterns by breaking down data into parts, all of which are inherently positive (non-negative).
This property makes NMF particularly useful for recommender systems, such as suggesting movies on streaming platforms based on users' previous ratings.
4. Spectral Clustering
Imagine connecting dots based on how closely related they are and then drawing boundaries to form groups of these connected dots.
This technique considers the data's connectivity and often finds applicability in social network analysis or image segmentation.
It can produce highly accurate clusters, especially when the shape of the clusters is not rounded or the data size is large.
5. Recommender Systems
If you've ever used an online service that suggests products or content tailored for you, you've encountered recommender systems. These Unsupervised Learning models analyze past behavior to predict items you might like, whether it's movies, music, or merchandise.
They crunch the collective data and highlight personal recommendations, strengthening customer service and user satisfaction.
Why is Unsupervised Learning Relevant?
Businesses face mountains of unlabeled data (documents, images, and numbers) with untapped potential. Unsupervised Learning steps in to sift through this data, helping group similar elements, identify anomalies, and uncover hidden relationships that traditional methods might miss.
Just imagine discovering new customer segments or understanding user behavior and preferences in a much faster way! This knowledge could empower you to make data-driven decisions that could lead to improved User Experience (UX).
In healthcare, it could help identify early signs of disease. For instance, Haohui, L. and Shahadat, U., in the study Unsupervised Machine Learning for Disease Prediction, they determined that UL algorithms like Density-Based Spatial Clustering of Applications with Noise (DBSCAN) have a powerful potential in disease prediction.
It’s important to highlight that the real value provided by UL is helping teams to get ahead, not replacing them. Having human experts to have your back and double-check the outcomes provided by these models is crucial for properly implementing them into your strategy.
Conclusion
Unsupervised Learning is an exciting and powerful field of AI that can uncover hidden patterns and relationships within data. From customer segmentation to medical scans, it has the potential to revolutionize the way we analyze and understand information in this era of Big Data.
