Clustering Models
Clustering is an unsupervised learning technique that groups similar data points together based on their features, without requiring labeled training data.
What is Clustering?
Clustering is the task of dividing data points into groups (clusters) such that data points in the same group are more similar to each other than to those in other groups. It's a main task of exploratory data analysis and a common technique for statistical data analysis.
Unlike supervised learning methods, clustering algorithms don't require labeled training data. Instead, they identify natural groupings in the data based on similarity measures.
Key Characteristics
- Unsupervised learning approach (no labeled data required)
- Groups data points based on similarity or distance measures
- Helps discover hidden patterns and structures in data
- Evaluated using metrics like silhouette score, Davies-Bouldin index, and inertia
- Used for customer segmentation, anomaly detection, and data preprocessing
Common Clustering Algorithms
A clustering algorithm that partitions n observations into k clusters where each observation belongs to the cluster with the nearest mean.
A method of cluster analysis which seeks to build a hierarchy of clusters, either using a bottom-up or top-down approach.
Common Applications
Grouping customers based on purchasing behavior, demographics, and preferences to target marketing campaigns.
Identifying outliers or unusual patterns in data that don't conform to expected behavior, useful for fraud detection.
Partitioning digital images into multiple segments to simplify representation and make analysis easier.
Evaluation Metrics
Clustering algorithms are evaluated using different metrics than supervised learning models. Common evaluation metrics include:
- Silhouette Score: Measures how similar an object is to its own cluster compared to other clusters.
- Davies-Bouldin Index: The average similarity between each cluster and its most similar cluster.
- Inertia: The sum of squared distances of samples to their closest cluster center.
- Calinski-Harabasz Index: The ratio of between-cluster dispersion to within-cluster dispersion.
- Adjusted Rand Index: Measures the similarity between the true labels and the clustering assignments.