14 May What is Clustering in Machine Learning

Clustering is an unsupervised learning technique where the goal is to group similar data points together based on their features without any prior labels.

What is Clustering

Clustering is the process of dividing a dataset into groups (called clusters) such that:

• Data points in the same cluster are similar to each other.
• Data points in different clusters are dissimilar.

Example: Imagine you have customer data (age, spending habits). Clustering can group them into:

• High spenders (young professionals)
• Moderate spenders (families)
• Low spenders (students)

 Similarity/Distance Metrics in Clustering

Clustering relies on measuring how “close” or similar two data points are. Common distance measures:

• Euclidean Distance (straight-line distance between points)
• Manhattan Distance (sum of absolute differences)
• Cosine Similarity (angle between vectors, useful for text)

Types of Clustering Algorithms

Let us see the types of clustering algorithms:

1. K-Means Clustering (Most popular)
   • Divides data into K clusters.
   • Works by iteratively assigning points to the nearest centroid (center of a cluster).
   • Requires specifying K beforehand (use the Elbow Method to find optimal K).
2. Hierarchical Clustering
   • Builds a tree-like structure (dendrogram) of clusters.
   • Two types:
     • Agglomerative (bottom-up, starts with single points and merges them).
     • Divisive (top-down, starts with one cluster and splits it).
3. DBSCAN (Density-Based Clustering)
   • Groups points in high-density regions.
   • Can find arbitrarily shaped clusters.
   • Does not require specifying the number of clusters.
4. Gaussian Mixture Models (GMM)
   • Assumes data is generated from a mixture of Gaussian distributions.
   • Uses probabilistic assignment to clusters.

Evaluating Clusters

Since there are no labels, we use internal evaluation methods:

• Silhouette Score (Means how well-separated clusters are, range: -1 to 1, higher is better).
• Inertia (Sum of squared distances to centroids, lower is better, used in K-Means).
• Davies-Bouldin Index (Lower values indicate better clustering).

 Challenges in Clustering

Here are some challenges in Clustering:

• Choosing the right K (number of clusters) → Use the Elbow Method or Silhouette Analysis.
• Sensitivity to outliers → DBSCAN is more robust than K-Means.
• Different scales of features → Always normalize/standardize data before clustering.
• Curse of dimensionality → High-dimensional data can be tricky; consider PCA for dimensionality reduction.

When to Use Clustering

• Customer Segmentation (Marketing)
• Anomaly Detection (Fraud detection)
• Image Segmentation (Computer Vision)
• Document Clustering (NLP)

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Read More:

• What is Classification
• Applications of Classification
• Types of Classification
• What is Regression
• Applications of Regression
• Types of Regression
• What is Model Evaluation
• Model Evaluation Terminologies
• Confusion Matrix in Machine Learning