Seven Clustering Algorithms Every Data Scientist Must Know

Seven Clustering Algorithms Every Data Scientist Must Know

In the field of data science, specific algorithms are used to group data points during the process of clustering. This machine learning (ML) technique helps data analysts quickly gain valuable insights from collected data.1 Clustering is a method of unsupervised learning, one of three approaches to machine learning, which is used for large, unstructured, and unclassified datasets.2

Machine language is highly efficient at classifying unstructured data. It’s also ideal if you don’t know the ground truth for labeling purposes.3 In these cases, ML models can be used to discover relevant patterns within a set of data. Examples of this could include detecting anomalies in network traffic, making content recommendations, and grouping customers based on common characteristics (customer segmentation).Using clustering algorithms, you can assemble locations and risk factors for an insurance company, or segment customer demographics for a marketing agency.2

Understanding how to perform and leverage clustering is an extremely valuable skill that stands out on a resume, whether you’re looking to move up in data science or switch careers. Keep reading to uncover the real-world uses for clustering algorithms and the most important methods with which every data scientist should be familiar.

Real-World Uses for Clustering Algorithms

Clustering and clustering algorithms are helpful when you have an enormous dataset with no labels. With this practice, you can create groups of similar data and organize them into more meaningful clusters for analysis and further manipulation.5

Here are some real-world examples of how cluster analysis is used by diverse industry sectors:

  • Retail Marketing – Variables such as household size and income can be organized by a clustering algorithm so that a company can send personalized advertisements.
  • Streaming Services – Clustering analysis can be used to identify frequent viewers so that a company can better target its advertising dollars.
  • Email Marketing – With cluster analysis of data such as the percentage of emails opened and time spent reading, a business can tailor email marketing campaigns based on consumer behavior.  
  • Health Insurance – Actuaries use cluster analysis to identify groups of customers that use their insurance in various ways. This helps them to set monthly premiums based on expected usage.6

Other clustering applications include recommendation engines, social network analysis, search result clustering, image processing, data mining, and biological data analysis.7

Key Clustering Algorithms & Their Pros and Cons

During the clustering process, abstract objects are collected into groups (clusters) of objects with similar properties or traits.7 There are many different types of clustering algorithms, and each has its advantages and disadvantages.

K-Means Clustering

This is the best-known of all the clustering algorithms, and it is frequently taught to students in data science and machine learning programs. It’s also one of the easier algorithms to understand and use to write code.1

To begin, select classes or groups to use and randomly initialize each of their center points. Before selection, look at the data and identify any clear groupings. Classify each data point by calculating the distance between the point and the group’s center, and place it in the group with the closest center.1

From the classified data points, recompute the group center from the mean of all vectors in that group. Repeat the steps for a predetermined number of iterations or until there isn’t a significant change in the group centers. Alternatively, you can initialize the groups’ centers randomly a few times, selecting a run that provides the best results.1

Advantages: K-Means is faster than other clustering algorithms, with fewer computations.1

Disadvantages: You must know and select how many groups or classes there are. Since the goal of clustering algorithms is to gain insight from the data, this isn’t ideal for most scenarios. Also, the K-Means process begins with a random selection of cluster centers and may provide different results from different algorithm runs. Thus, results may not be consistent or repeatable.1 

Mean-Shift Clustering

This centroid-based algorithm, based on a sliding window, tries to find dense sectors of data points and locates center points for each group or class. It does this by updating candidates from the mean (average) of those points within its sliding window. Near-duplicates are eliminated to form a final set of center points and their groups.1

Advantages: You do not have to pre-select the number of clusters. The mean-shift technique automatically discovers them. Cluster centers move toward the areas of maximum density.1

Disadvantages: The selection of the window size and radius is critical.1

Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

As a density-based clustering algorithm, DBSCAN is similar to mean-shift clustering, but it offers important advantages over other types of algorithms. It begins with an arbitrary, unvisited, starting data point. Its neighborhood is extracted with a distance epsilon ε, and all points within that distance are neighborhood points. If there are enough points in this neighborhood, clustering begins, and the current point is the first data point in that new cluster. If not, the point is considered noise, though it may become part of the cluster in the future.1

Advantages: DBScan doesn’t require a preset number of clusters. It identifies outlying data points as noise, rather than including them in a cluster, even if it is different. This algorithm is adept at finding arbitrarily shaped and sized clusters.1

Disadvantages: This algorithm doesn’t perform well with clusters of varying density, nor with high-dimensional data since the distance threshold and midpoints vary and are hard to estimate.1

Expectation-Maximization (EM) Clustering using Gaussian Mixture Models (GMM)

While K-Means clustering can’t handle data clusters that either have mean values that are close together or are not circular, GMMs provide greater flexibility. With this type of clustering algorithm, it’s assumed that the data points are Gaussian distributed. Therefore, clusters can be of any elliptical shape and each Gaussian distribution becomes part of a single cluster. The optimization algorithm EM is used to find the parameters for each cluster (mean and standard deviation).1

Advantages: GMMs are far more flexible than K-Means for cluster covariance. With the standard deviation parameter, clusters can be any ellipse shapes, rather than only circles. GMMs can also have multiple clusters (mixed membership) for a single data point since they use probabilities.1

Disadvantages: GMM tends to be slower than K-Means since it requires more iterations to reach convergence.8

Agglomerative Hierarchical Clustering

Hierarchical clustering algorithms can be either top-down or bottom-up. The bottom-up algorithm treats every data point as a cluster and then merges (agglomerates) pairs of clusters until they all are merged into a single cluster containing all the data points. This is also known as hierarchical agglomerative clustering (HAC), represented as a tree or dendrogram. The “root” of the tree is the unique cluster that gathers the samples, and the leaves are the clusters with just one sample.1

Advantages: With HAC, you don’t need to specify the number of clusters, and you can select which number looks the best. This chosen distance metric is not critical—all tend to work well. Hierarchical clustering is helpful when the data has a hierarchical structure and you wish to recover that hierarchy.1

Disadvantages: Hierarchical clustering is more complex, takes more time, and is less efficient than GMM and K-Means.1

Spectral Clustering

Spectral Clustering, a graph clustering algorithm, is a method for clustering data that uses the eigenvalue decomposition of a matrix, which is a factorization theorem in matrix theory. The data clustering challenge is converted to graph form, graph clusters are identified, and then translated back. This lets you group nodes with a similar connection. The eigengap heuristic provides guidelines as to the number of clusters to choose.9

Advantages: Clusters are not assumed to be of a particular shape or distribution. It can perform equally well with a variety of data shapes. It does not require the actual data set, but just the similarity and distance matrix or the Laplacian. Therefore, it can cluster one-dimensional data.9

Disadvantages: You must choose the number of clusters, but heuristic can be used. It may be expensive to compute using this method, but frameworks and algorithms are available.9

Balance Iterative Reducing and Clustering using Hierarchies (BIRCH)

BIRCH clustering is scalable and intended for very large data sets. This method is based on the Clustering Feature (CF) notation of a CF tree that stores the features for hierarchical data clustering. The cluster of data points is represented by three numbers—N, LS and SS. N is the number of items in the subcluster, LS is the linear sum of the points, and SS is the sum of the square of the points. Each leaf node of the tree is a subcluster and not a data point, making it a compact structure.10

Advantages: BIRCH is designed for very large data sets. It can cluster with just one scan of the database, and further scans improve the quality.10 BIRCH can also cluster multi-dimensional, incoming metric data points to generate high-quality clustering despite any time or memory limitations. It’s a clustering algorithm that can efficiently handle noise—that is, data points that do not belong to an underlying pattern.11

Disadvantages: This type of clustering can only handle numeric data.10

How to Select the Right Algorithm

So, which of the clustering algorithms should you use? It will depend on the type of data you’re looking at and the specific application. Take a close look at the capabilities and output of each using our information above, then assess how and when your team can fully use them. With any of these clustering techniques and clustering algorithms, you can quickly perform basic analysis to provide company executives with important intelligence to benefit their business.2 

Hone Your Data Analysis Skills for a Future-Proof Career

Having expertise in machine learning techniques and other data science skills gives you an edge in today’s high-tech business environment—and well into the future. The job market for operations research analysts is projected to grow 25% by 2030, and it’s not looking to slow down anytime soon.12 

The good news is that you can become a leader in the data science revolution without pausing your career. By taking comprehensive online data science classes from EmergingEd, you can gain skills in machine learning, artificial intelligence, modeling, and data visualization in just eight weeks. Once the course is done you can introduce new and better methods into your work—and look for the next EmergingEd course to build your expertise.



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