Clustering

Introduction

1. K-Means Clustering (Partition-Based Clustering)

How It Works

Advantages

• Efficient and scalable for large datasets.

• Works well for spherical and well-separated clusters.

• Easy to implement and interpret.

Disadvantages

• Requires predefining K, which is not always known.

• Sensitive to outliers, as extreme values can distort centroids.

• Performs poorly with non-spherical clusters or clusters of varying densities.

Best Use Cases

• Customer segmentation

• Image compression and pattern recognition

• Market analysis

2. Hierarchical Clustering

How It Works

• Unlike K-Means, hierarchical clustering does not require K beforehand.

• It builds a tree-like structure (dendrogram) that represents cluster relationships.

• There are two main approaches:

  • Agglomerative (Bottom-Up): Each data point starts as its own cluster and merges iteratively.

  • Divisive (Top-Down): Starts with all data points in one cluster and recursively splits them.

Advantages

• No need to predefine K, as the dendrogram helps determine the best number of clusters.

• Captures hierarchical relationships in data.

• Can work with small datasets where computational cost is not a concern.

Disadvantages

• Computationally expensive for large datasets (O(n²) complexity).

• Once merged, clusters cannot be split again (agglomerative approach).

• Sensitive to noise and outliers.

Best Use Cases

• Gene expression analysis

• Document clustering

• Social network analysis

3. DBSCAN (Density-Based Clustering)

How It Works

• Unlike K-Means and Hierarchical Clustering, DBSCAN does not require K.

• It defines clusters based on density:

  • Core Points: Have a minimum number of neighbors (min_samples) within a defined distance (eps).

  • Border Points: Close to core points but do not satisfy density conditions.

  • Noise Points: Outliers that do not belong to any cluster.

• Expands clusters from core points and ignores noise.

Advantages

• Does not require K to be predefined.

• Handles noise and outliers well.

• Works well with arbitrarily shaped clusters.

Disadvantages

• Parameter tuning (eps, min_samples) can be difficult.

• Does not work well if clusters have varying densities.

• Can be slow for large datasets.

Best Use Cases

• Anomaly detection (fraud, cybersecurity)

• Geographic data clustering

• Noise filtering in large datasets