Clustering

What is Data Clustering?

Data clustering is a widely used technique based on machine learning. This technique is used for segregating groups of abstract objects into classes of similar objects.

Clustering is an integral part of data science. It allows data to be divided into various subsets or clusters. Each of these clusters is composed of data objects characterized by:

  • High inter-similarity (similar characteristics within a cluster)
  • Low intra-similarity (dissimilar characteristics between different clusters)

Clustering serves as a powerful tool for data analysis. It can reveal the inherent structure of the data and help identify patterns and insights that may otherwise be overlooked.

Clustering Methods/ Data Clustering Algorithms

There are different clustering approaches that can be used to handle various types of data:

  • Hierarchical-based Clustering: Hierarchical clustering is ideal for hierarchical data, like from a company database. It constructs a cluster tree, organizing everything from the top-down, facilitating structured organization. This approach is sometimes called connectivity-based clustering.
  • Partitioning/Centroid-Based: Centroid-based clustering separates data points using multiple centroids. Each point is assigned to a cluster based on its squared distance from the centroid. This method is widely used in clustering.
  • Density-based Clustering: Density-based clustering groups data based on areas of high data point concentrations, surrounded by low concentrations. It identifies dense data regions, allowing clusters of any shape and ignores outliers. This is most commonly used approach.
  • Distribution-based Clustering: Distribution-based clustering categorizes data points based on their likelihood of belonging to the same probability distribution (e.g., Gaussian, Binomial). Each cluster is defined by a statistical distribution, with a central point influencing the probability of inclusion for each data point. Unlike density and boundary-based methods, distribution-based clustering avoids the need for a priori cluster specification and provides flexibility in defining cluster shapes.
  • Fuzzy Clustering: The process employs a weighted centroid based on spatial probabilities, involving initialization, iteration, and termination. It generates clusters viewed as probabilistic distributions instead of hard labels, assigning membership values based on distances. Fuzzy clustering accommodates ambiguous situations, prioritizing probabilities over distances.
  • Grid-based clustering: divides data into cells in a grid. It quantizes the space, allowing efficient processing. Clusters form in dense cells, simplifying analysis and facilitating outlier detection.
  • Model-based clustering: Model-based clustering assigns data points to clusters based on probabilistic models. It assumes the data is generated by a mixture of underlying distributions, optimizing model parameters for accurate clustering.
  • Constraint-based clustering: Distribution-based clustering categorizes data points by the probability of belonging to a specific distribution (e.g., Gaussian). Each cluster, influenced by a statistical distribution's central point, allows flexibility without a predetermined cluster specification.

Clustering Algorithms

Clustering has a wide range of applications across multiple domains, such as market research and customer segmentation, pattern recognition, image processing, social network analysis and anomaly detection.

Some of the most common cluster algorithms include:

  • K-Means
  • Agglomerative Hierarchical Clustering
  • BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies)
  • DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
  • Affinity Propagation
  • Mini-Batch K-Means
  • Mean Shift Clustering
  • Gaussian Mixture Models (GMM)

Additional Resources