When training an AI model, three primary learning methods stand out: supervised learning, unsupervised learning, and reinforcement learning. In this article, I’ll give you a comprehensive guide to unsupervised learning, including its definition, mechanisms, various types, and its advantages and disadvantages.
What is unsupervised learning?
Unsupervised learning is a type of AI learning where the AI model is trained using unlabeled data. Unlike supervised learning, which relies on labeled data to train AI models, in unsupervised learning, we provide the AI model with a dataset that contains only input data (features) and without target values or classes. The primary objective of unsupervised learning is to discover the relationships and patterns within the data and group data points based on them.
“This is like giving a child a set of images containing both cats and dogs and asking them to group similar images. The child, in this case, would look for similarities in the images and likely organize the images of cats in one group and the images of dogs in another, without necessarily recognizing that they represent cats and dogs.”
How does unsupervised learning work?
In Unsupervised Learning, AI models try to discover inherent structures, relationships, and clusters within data. In other words, These models aim to identify patterns within training datasets by autonomously extracting useful information or features and categorizing input objects based on the recognized patterns.
For example, consider inputting a dataset containing images of animals into an unsupervised learning AI model.
The AI model attempts to categorize the animals into groups based on features such as fur, scales, and feathers without any guidance. As it becomes proficient at identifying distinctions within each category, the model further subdivides the images into more specific clusters.
There are many unsupervised learning techniques, including clustering, dimensionality reduction, and association rule learning. The way an AI model classifies data into classes, the number of classes, and the criteria for dividing input data into classes vary depending on the specific technique used.
Types of unsupervised learning algorithms
Unsupervised learning can be categorized into three main tasks: clustering, association rules, and dimensionality reduction.
Clustering
Clustering is a method of grouping unlabeled data based on their similarities or differences. These algorithms identify commonalities among data objects and categorize them based on the presence or absence of these commonalities. When two instances are placed in different groups, it implies they have dissimilar properties.
Here are different types of clustering algorithms:
- Exclusive Clustering
- Overlapping Clustering
- Hierarchical clustering
- Probalistic clustering
Exclusive clustering
- In this clustering method, Data are grouped in such a way that one data point can exist only in one cluster. This can also be referred to as “hard” clustering.
- Example: K-means
Overlapping clustering
- Overlapping clusters allow data points to belong to multiple clusters with different levels of membership.
- Example: Fuzzy C-Means(FCM)
Hierarchical clustering
- Hierarchical clustering is a clustering algorithm used to group similar data points into nested clusters. It creates a hierarchical structure of clusters, with clusters at one level containing clusters from the previous level. This results in a tree-like structure called a dendrogram that displays the relationships between data points and clusters. The algorithm terminates based on a predefined stopping criterion, such as a specified number of clusters or a certain level of dissimilarity between clusters.
- There are two main types of hierarchical clustering:
- Agglomerative Hierarchical Clustering: This is a bottom-up approach where initially, each data point is considered its own cluster. Subsequently, pairs of clusters are iteratively merged based on their similarity until all data points belong to a single cluster or a predefined number of clusters. The algorithm progresses by continually merging the clusters that are closest in terms of a chosen distance metric, such as the Euclidean distance.
- Divisive Hierarchical Clustering: This is a top-down approach where all data points initially belong to a single cluster. The algorithm recursively divides the data into smaller clusters based on the dissimilarities between data points. This process continues until each data point is in its own cluster or a predetermined number of clusters is reached. Divisive clustering, while less common, is still noteworthy within the context of hierarchical clustering. These clustering processes are typically visualized using dendrograms, which are tree-like diagrams documenting the merging or splitting of data points at each iteration.
Probabilistic clustering
- In probabilistic clustering, data points are clustered based on the likelihood that they belong to a particular distribution.
- Example: Gaussian Mixture Model (GMM)
It’s important to note that you can adjust the number of clusters your algorithms should identify, allowing you to control the granularity of these groups
Some of the commonly used clustering algorithms include the K-means algorithm, K-Nearest Neighbors clustering, the Fuzzy K-means algorithm, hierarchical clustering, and density-based clustering algorithms.
K-means Clustering
- K-means clustering is a common example of an exclusive clustering method, where data points are allocated into K clusters, with K representing the number of clusters based on the distance from each group’s centroid. Initially, we choose the desired number of clusters (k).
- In K-means clustering, data points are grouped under the same category as the centroid they are closest to. A larger K value results in smaller, more granular groups, while a smaller K value leads to larger groups with less granularity. K-means clustering finds applications in market segmentation, document clustering, image segmentation, and image compression.
- K-means clustering can be further divided into two subgroups:
- Agglomerative clustering:
- In this type of K-means clustering, a fixed number of clusters is predetermined, and all data points are allocated to those clusters. Unlike the traditional K-means method, this variant does not require the user to specify the number of clusters,
- The agglomeration process begins by initially treating each data point as its own cluster. Using a distance measure, it iteratively merges clusters, reducing the total number of clusters by one in each iteration. Eventually, this process results in one large cluster that contains all the objects.
- Dendrogram:
- In Dendrogram clustering, each level represents a potential cluster, and the height of the dendrogram indicates the level of similarity between two merging clusters. Closer to the bottom of the dendrogram, you’ll find more similar clusters. However, determining the natural or appropriate grouping from a dendrogram is often subjective.
- Agglomerative clustering:
K- Nearest neighbors
- K-nearest neighbors (K-NN) are one of the simplest machine learning classifiers. Unlike other machine learning techniques, K-NN doesn’t create a model. Instead, it’s a straightforward algorithm that stores all available cases and classifies new instances based on a similarity measure. It excels when there is a distinct distance between examples.
Association Rule Mining
Association rules are a data mining technique used in unsupervised machine learning to discover interesting relationships between features (items) in a dataset. They take a rule-based approach to identify strong associations between items based on their co-occurrence, thereby revealing patterns and relationships within the data.
Several algorithms are used for association rule learning, with the most well-known being the Apriori algorithm. Other algorithms, such as Eclat and FP-growth, are also utilized to identify frequent item sets and association rules within the dataset.
Apriori Algorithm
- The Apriori algorithm is widely used in market basket analysis and recommendation engines. It identifies frequent itemsets and predicts the likelihood of consuming one product given the consumption of another. For example, in a music streaming platform, if a user listens to a song by one band, the Apriori algorithm can suggest songs from similar bands based on user preferences.
Association rule mining is commonly used in market basket analysis, which helps retailers understand customer purchasing patterns by uncovering relationships between different products. For example, it can reveal that people who buy bread are also likely to purchase butter or jam.
Also, these rules can be applied to various scenarios, such as grouping cancer patients based on their gene expression measurements, segmenting shoppers based on their buying histories, or categorizing movies based on viewer ratings.
Dimensionality Reduction
High-dimensional datasets, containing numerous features or dimensions, present challenges such as difficulties in effective data visualization, increased computational complexity for machine learning algorithms, and the risk of overfitting—commonly referred to as the ‘curse of dimensionality’.
Dimensionality reduction in unsupervised learning is a technique that reduces the number of features while preserving meaningful information. It transforms data from high-dimensional to lower-dimensional spaces without compromising the original data’s inherent properties. The primary objective of dimensionality reduction is to simplify data for better analysis, This technique is particularly valuable during exploratory data analysis (EDA) and as part of data preprocessing to prepare the data for modeling.
Dimensionality reduction techniques are commonly implemented using algorithms such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and autoencoders.
- Principal Component Analysis (PCA): PCA is a widely used dimensionality reduction technique that focuses on reducing redundancies in the data and compressing datasets through feature extraction. It does so by performing a linear transformation to create new data representations known as “principal components.” These components capture the directions in the data that maximize variance, making them the most informative directions.
- Singular Value Decomposition (SVD): SVD is another dimensionality reduction approach that factorizes a matrix into three low-rank matrices. It is denoted by the formula A = USVT, where U and V are orthogonal matrices, and S is a diagonal matrix containing singular values. Like PCA, SVD is used to reduce noise and compress data, making it valuable in various applications, including image processing.
- Autoencoders: Autoencoders are a type of neural network-based dimensionality reduction method. They compress input data into a lower-dimensional representation within a hidden layer (encoding) and then reconstruct the original data from this compressed representation (decoding).
Advantages of unsupervised learning
Unsupervised learning is a valuable tool in machine learning and data analysis due to the following advantages:
- Unsupervised learning helps discover hidden patterns, structures, and relationships within data, which may not be apparent through manual inspection.
- Unsupervised learning is a powerful tool for Exploration and understanding of data characteristics and structure, especially when data lacks clear labels or categories.
- Unsupervised learning Reduced bias compared to supervised learning, as it doesn’t rely on potentially biased labels, making it important for fairness and ethical considerations.
- Unsupervised learning algorithms are versatile in applying algorithms to various data types and domains without the need for predefined labels.
- Unsupervised learning can identify novel or previously unseen patterns or outliers, which is valuable in various applications, including fraud detection and rare event prediction.
- Dimensionality reduction techniques, such as t-SNE, enable visual exploration of high-dimensional data, making it easier for analysts to understand data distributions and patterns.
Disadvantages of unsupervised learning
While unsupervised learning has advantages, it’s also important to note that Unsupervised learning has several disadvantages and challenges that you should consider:
- Unsupervised learning doesn’t use labeled data, making it challenging to assess result quality and model performance.
- Unsupervised learning results can be challenging to interpret, as the discovered patterns may need clearer and more intuitive meanings.
- Cluster or pattern interpretation can be subjective and context-dependent, necessitating domain expertise for meaningful insights.
- Many unsupervised learning algorithms have parameters that require careful tuning, impacting result quality.
- Input data quality significantly affects unsupervised learning results. Noisy or inconsistent data can lead to inaccuracies.
- Choosing the appropriate unsupervised learning algorithm for a specific task can be challenging, as different algorithms may perform better on different data types.
Application of unsupervised learning
Unsupervised learning offers valuable solutions across various domains, with applications that include:
- Clustering: Grouping similar data points, used in marketing for customer segmentation, cybersecurity for anomaly detection, and text corpora organization.
- Dimensionality Reduction: Reducing data dimensions while preserving essential features, applied for data visualization, feature selection, and data compression.
- Recommendation Systems: Making personalized recommendations based on user behavior, such as movie and music recommendations or product suggestions.
- Anomaly Detection: Identifying unusual patterns in data is essential for fraud detection, intrusion detection, and predicting equipment failures.
- Market Basket Analysis: Informing retail inventory management and product placement by discovering associations between purchased products.
These are just a few examples of the many applications of unsupervised learning. The versatility of unsupervised learning makes it a valuable tool for discovering insights and patterns within large and complex datasets, often leading to improved decision-making and automation in various domains.
Unsupervised learning, a cornerstone of machine learning, plays a pivotal role in data analysis and pattern discovery. It offers distinct advantages, such as uncovering hidden patterns in unstructured data, addressing dimensionality challenges, and identifying novel patterns without the crutch of labeled data. However, it introduces complexities, including result unpredictability and interpretability demands. Its applications span a spectrum of fields, from customer segmentation to genomics and autonomous driving, enabling data analysts and machine learning practitioners to extract insights, automate decisions, and propel technological advancements. In essence, unsupervised learning is integral to artificial intelligence and data science, facilitating knowledge extraction from intricate data and fostering innovation in the realms of data analysis and machine learning.