Citizen Data Scientist, Module V: Unsupervised Learning: Discovering hidden patterns
While supervised learning is about learning from labeled data, unsupervised learning is where models work with unlabeled data to uncover hidden patterns. This post dives into the essential concepts of unsupervised learning, including clustering, dimensionality reduction, and anomaly detection, with real-world examples to make these concepts more relatable and intuitive.
What is Unsupervised Learning?
Unsupervised learning involves training models on data that doesn’t have predefined labels. Unlike supervised learning, where models know what they're aiming for, unsupervised learning works with raw data to find patterns or groupings on its own.
For example, imagine you have a pile of coins and no knowledge of which are pennies, nickels, or dimes. By observing their characteristics like size and weight, you could naturally group them into categories. That’s what unsupervised learning does—finding structure in unlabeled data
Clustering: Grouping Data Intuitively
Clustering is one of the most common tasks in unsupervised learning. It groups data points so that those in the same group are more similar to each other than to those in other groups. One popular algorithm for this is K-means clustering.
How K-means Clustering Works:
Initialization: Choose the number of clusters, kkk, and randomly place initial centroids.
Assignment: Assign each data point to the nearest centroid.
Update: Recalculate centroids as the mean of all points in the cluster.
Repeat: Continue the assign-update process until centroids stabilize.
Clustering Penguins
If you have data about penguins (e.g., bill length, flipper length, body mass), K-means clustering can group them by species without knowing the labels in advance. This is helpful for discovering natural groupings in datasets
Choosing the Right Number of Clusters: The Elbow Method
Determining the number of clusters in K-means can be tricky. The elbow method helps by plotting inertia (the sum of squared distances between data points and their centroids) against the number of clusters.
Inertia decreases as more clusters are added.
The elbow point is where adding another cluster doesn’t significantly reduce inertia. This indicates the optimal number of clusters.
If you're clustering penguins based on physical traits, plotting inertia versus the number of clusters will show an “elbow” where the reduction in inertia starts to level off. This is a good indicator of how many natural clusters are present in the data
Dimensionality Reduction: Simplifying Complex Data
High-dimensional data can be challenging to work with and visualize. Dimensionality reduction simplifies datasets by reducing the number of features while keeping the most important information.
One popular method is Principal Component Analysis (PCA), which finds new axes (principal components) that maximize the variance in the data.
How PCA Works:
Standardization: Ensure all features have the same scale.
Identify Principal Components: Find directions where the data varies the most.
Project Data: Use the principal components to reduce the number of dimensions.
If you have four features in your penguin dataset, PCA can reduce this to two principal components, allowing for easier visualization and analysis while retaining most of the data's variance
Anomaly Detection: Spotting the Outliers
Anomaly detection is used to find data points that don’t fit the norm. This is crucial for applications like fraud detection, where outliers could represent suspicious transactions.
A useful algorithm for anomaly detection is DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Unlike K-means, DBSCAN doesn’t need the number of clusters specified beforehand. It identifies dense regions of data and labels points outside these regions as anomalies.
Detecting Credit Card Fraud
Banks use anomaly detection to flag transactions that don’t align with a customer's usual spending behavior. For instance, if a card is suddenly used for a large purchase in another country, anomaly detection can identify this as a potential case of fraud
Key Takeaways: When to Use Unsupervised Learning
Clustering: Ideal for grouping data when labels are not available (e.g., customer segmentation, exploring biological data).
Dimensionality Reduction: Reduces complexity and improves model performance or data visualization.
Anomaly Detection: Useful for finding rare or unexpected patterns in data (e.g., fraud detection, quality control).
Unsupervised learning is essential for exploring and understanding complex datasets. Although it doesn’t directly predict outcomes, it provides powerful tools for discovering hidden patterns that can inform decisions and guide further analysis.
End-to-End Example: Clustering and Dimensionality Reduction
To wrap it up, here’s a simplified end-to-end process for unsupervised learning using the penguin dataset:
Data Collection:
Gather data with features like bill length, flipper length, etc.
Data Preprocessing:
Standardize the features to ensure they’re on the same scale.
Dimensionality Reduction (Optional):
Apply PCA to reduce the number of features for easier clustering.
Clustering:
Use K-means or another clustering algorithm to group data points.
Apply the elbow method to decide the number of clusters.
Anomaly Detection (Optional):
Use DBSCAN or another algorithm to find and label outliers.
Insights:
Analyze the clusters and anomalies to draw meaningful conclusions about the data.