Types of Clustering

Written by Coursera Staff • Updated on

Learn more about types of clustering. Examine the various clustering methods, such as distribution-based clustering, fuzzy clustering, and more.

[Featured Image] A businesswoman analyses data reports created with different types of clustering as she sits at her computer screens in her home office.

Clustering is a fundamental component of machine learning and cluster analysis techniques in data sciences. The process works effectively by finding similar structures in a group of unlabeled data. In the accessible heterogeneous data sets, it identifies subgroups where each cluster is more homogeneous than the sum of the parts. 

These clusters are collections of identical objects distinct from those found in other clusters. The partitioned data can then be used for different business operations.

Types of clustering

Various types of clustering techniques are used in data analysis: connectivity-based, constrained, centroid-based, density-based, distribution-based, and fuzzy. Each one offers different benefits depending on the goal of the study. Clustering is used in other fields for various purposes. These include:

  • Marketing: Utilising clustering in marketing strategies can help businesses segment and profile customers. This gives companies a better understanding of their target audience and enables them to develop campaigns and content that is more specific to them.

  • Biology: Clustering techniques can be leveraged to classify different species of plants and animals accurately.

  • Library science: It groups books according to the topics and information. 

  • Security: It spots fraud, spam, fake news, and unwanted content. 

  • Real estate: It groups homes and analyses their values based on physical locations and other relevant factors.

Connectivity based clustering

The idea behind connectivity-based clustering is that the vectors of data points in space show more similarity to one another than the data points farther apart. The maximum distance required to connect cluster components can be used to describe a cluster in large part. 

The term "hierarchical clustering" derives from the fact that different clusters will form at various distances and can be visualised using a dendrogram. These algorithms offer a vast cluster hierarchy that merges at specific distances rather than a single data set partitioning. 

In a dendrogram, the objects are arranged on an x-axis to prevent clusters from merging. The y-axis represents the distance between clusters at which they eventually come together. The distance between similar data objects and those in the same cluster is minimal, while the hierarchy is further up for the non-identical objects. 

Data objects mapped are cluster-related regarding discrete characteristics like cross-tabulation, multidimensional scaling, and quantitative relationships between data variables. 

Centroid-based clustering

Centroid-based clustering groups the data based on their proximity to a defined value. K-means is a standard centroid-based algorithm and is a high-performance, easy-to-use approach to clustering. The main underlying theme of all centroid-based algorithms is calculating the distance between the objects in the data set under consideration. The fundamental aspect of distance measurement is typically derived using one of the Manhattan, Minkowski, or Euclidean distance measuring mechanisms. 

Density-based clustering

This method clusters data together in high-density groups. The resultant shapes are arbitrary and not uniform. Noise or outliers are left out of the clusters, so they comprise the points located within the sparse separating areas. 

DBSCAN is a well-known type of density-based clustering algorithm. DBSCAN is an acronym for density-based spatial clustering of applications with noise and is widely used. It relies upon a cluster concept based on the number of objects within a radius (density). 

Constrained clustering 

Constrained clustering groups data while incorporating some constraints. The constraints are typically known as pairwise statements, which state whether or not we can group two items into the same cluster. Constraints can be divided into several categories, such as:

  • Constraining the choice of clustering parameters: The user can specify a preferred area for each clustering parameter.

  • Constraining specific objects: Constraints are defined for objects that will be clustered.

  • User constraining the properties of each cluster:  A user can direct the clustering process by specifying their preferred features for the resulting clusters.

Distribution-based clustering

This method groups data based on distribution methods, such as Gaussian, binomial, or normal distributions. Components of this clustering technique include a centre point and data close to it—the farther away data is from the centre point, the less likely it will be included in the cluster. 

Regarding correctness, flexibility, and cluster shapes, distribution-based clustering is advantageous over centroid-based and proximity-based methods. It works best with simulated data and fits easily into a distribution.

Fuzzy clustering 

Fuzzy clustering is a soft clustering technique whereby points closer to a centre are likelier to be in the cluster than points farther from the centre. The fuzzy c-means algorithm (FCM) is this approach's most widely used algorithm. A single data point can belong to more than one cluster in this scenario. The result is the probability that a given data point will belong to each cluster. 

The chances of a point coming into a specific grouping range from zero to one. The overall mean of all points in this situation, weighted by the likelihood that they correspond to the cluster, is used to determine the centre of a cluster.

Next steps

If you’d like to increase your knowledge of clustering algorithms and gain a certificate to add to your resume, consider completing the Clustering Geolocation Data Intelligently in Python Guided Project offered by Coursera Project Network on Coursera. 

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