14 May Types of Clustering in Machine Learning

Clustering is an unsupervised learning technique used to group similar data points together based on their features. The goal is to maximize intra-cluster similarity and minimize inter-cluster similarity.

Types of Clustering in Machine Learning

Clustering can be broadly categorized into different types based on methodology and application. These types are not the same as clustering algorithms but rather represent different clustering approaches. The algorithms fall under these types:

• Partitioning Clustering
• Hierarchical Clustering
• Density-Based Clustering
• Model-Based Clustering
• Grid-Based Clustering
• Fuzzy Clustering
• Distribution-Based Clustering

Let us understand them one by one:

Partitioning Clustering

• Divides data into non-overlapping clusters.
• Requires specifying the number of clusters (*k*) beforehand.
• Examples of Algorithms:
  • K-Means (Most popular)
  • K-Medoids (PAM)
  • CLARA (For large datasets)

Hierarchical Clustering

• Builds a tree-like structure (dendrogram) of clusters.
• Can be Agglomerative (bottom-up) or Divisive (top-down).
• No need to predefine the number of clusters.
• Examples of Algorithms:
  • Agglomerative Hierarchical Clustering
  • DIANA (Divisive Analysis)

Density-Based Clustering

• Forms clusters based on dense regions of data points.
• Can find arbitrarily shaped clusters and handle noise.
• Examples of Algorithms:
  • DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
  • OPTICS (Ordering Points To Identify Clustering Structure)
  • HDBSCAN (Hierarchical DBSCAN)

Model-Based Clustering

• Assumes data is generated from a statistical model (e.g., Gaussian distributions).
• Uses probabilistic approaches to assign clusters.
• Examples of Algorithms:
  • Gaussian Mixture Models (GMM)
  • Expectation-Maximization (EM)

Grid-Based Clustering

• Divides data space into a finite number of grids/cells.
• Works well for large datasets.
• Examples of Algorithms:
  • STING (Statistical Information Grid)
  • CLIQUE (Clustering in QUEst)

Fuzzy Clustering

• Allows data points to belong to multiple clusters with varying degrees of membership.
• Examples of Algorithms:
  • Fuzzy C-Means (FCM)

Distribution-Based Clustering

• Assumes data points belong to a probability distribution (e.g., Gaussian).
• Examples of Algorithms:
  • Mean-Shift Clustering

Are Clustering Types the Same as Algorithms?

• No, the types represent different clustering approaches.
• Algorithms are specific implementations under these types.
  • Example: K-Means is an algorithm under Partitioning Clustering, and DBSCAN is under Density-Based Clustering.

Here is the summary:

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