Clustering and cluster analysis: an explainer

Clustering is a technique used in several areas of computer science – such as machine learning, artificial intelligence, data analysis, and data mining – to group similar data points based on the information contained within them, such as characteristics or features. This approach is particularly useful when dealing with large-scale datasets that contain unstructured, unlabelled, or incomplete data.

Finding groups – or clusters – of similar data points ahead of analysis can help data analysis in a number of ways:

• Recognising patterns. Clustering can reveal patterns and structures in data that may not be immediately apparent. By grouping together similar data points into a number of clusters, it can help identify underlying relationships or trends in the data.
• Summarising data. Clustering can be used to summarise large datasets, making it easier to visualise and interpret the information. Rather than analysing each data point individually, clustering can help identify key characteristics in the data and group them together.
• Detecting data outliers. Clustering can help identify outliers, or data points that are significantly different from the rest of the data in the set. This can be useful in detecting errors in data, or identifying unusual behaviour.
• Pre-processing data. Clustering can be used as a pre-processing method before other data analysis techniques, such as regression analysis, are applied. This is because grouping together similar data points decreases the complexity of the data, which in turn can help improve the performance of other data analysis methods. 

As Google points out, clustering data is the first step towards understanding a dataset within a machine learning system:

“As the examples are unlabelled, clustering relies on unsupervised machine learning,” Google explains. “If the examples are labeled, then clustering becomes classification.”

Types of clustering algorithms

There are a number of cluster algorithms used in data analysis. Some examples include:

• Hierarchical clustering. These iterative algorithms build a hierarchy of clusters by continually dividing a dataset into smaller subsets. These divisions can be based on different metrics, segmentations, and variants, such as Euclidean distance, pairwise distance, time series, clustering coefficient, and so on.
• Partition-based clustering. These algorithms, also known as partitioning or centroid-based clustering, partition a dataset into what’s known as k clusters, where k is a predefined number. The most popular algorithm for centroids or partition-based clustering is k-means clustering. The k-means algorithm partitions the dataset into k clusters based on the mean value of the data points in each cluster.
• Density-based clustering. These algorithms identify clusters based on areas of high density in the dataset space. The most popular algorithm for density-based clustering is called DBSCAN (Density-Based Spatial Clustering of Applications with Noise).
• Model-based clustering. Also known as distribution-based clustering, these algorithms assume that data is generated from a probabilistic model, and use this model to identify clusters. The most popular algorithm for model-based clustering is known as Gaussian mixture models (GMM).
• Fuzzy clustering. These algorithms assign data points to clusters based on the degree of membership of each data point to each cluster. The most popular algorithm for fuzzy clustering is known as fuzzy c-means clustering.

What is information-driven clustering?

Information-driven clustering – or data-driven clustering – is a data analysis methodology that groups data points based on the information they contain, rather than their similarity. 

In information-driven clustering, the algorithm attempts to identify the most informative features in the data, and then clusters data points based on the information contained within those features. This can help identify patterns and relationships in the data that may not be immediately apparent.

What is the difference between hierarchical and information-driven clustering?

Hierarchical clustering and information-driven clustering are two distinct techniques. While both clustering methods aim to group similar data points together, they differ in their approach and the type of clustering results they produce.

Hierarchical customer groups data points together based on their similarity, forming a tree-like structure known as a dendrogram. In this technique, each data point is initially considered as a separate cluster, and then the algorithm continually merges clusters that are similar until all data points are part of a single large cluster.

The result of hierarchical clustering is a dendrogram that shows the relationships between the different clusters. This dendrogram is useful in visualising the clustering structure, and allows the user to see how closely related the different clusters are.

Information-driven clustering, meanwhile, groups data points based on the information they contain, rather than based on their similarity. Unlike hierarchical clustering, information-driven clustering does not produce a dendrogram, and the clusters are not necessarily organised in a hierarchical structure.

The benefits of clustering 

Clustering supports data analysis in several ways, including:

• Better understanding of data. By grouping similar data points together and identifying patterns that might not be immediately obvious, clustering helps analysts better understand complex datasets. 
• Improved decision-making. Clustering can help businesses and organisations identify trends or other important patterns, which in turn can provide validation, and inform optimal business decisions in areas such as product development or marketing strategies.
• Time-saving. Clustering can save a lot of time. Rather than analysing each data point individually, clustering groups similar data points together, which means that large datasets can be analysed more quickly and efficiently.

Applications for clustering

The applications for clustering are virtually limitless, and they are becoming more important as data use continues to grow. As the Institute of Electrical and Electronics Engineers (IEEE) pointed out at its International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) in 2017: “Data is the goldmine in today’s ever competitive world.”

Marketing

Many businesses use clustering to support their marketing activities. For example, clustering can be used to group customers based on data such as their:

• Shopping preferences.
• Online engagement and behaviours.
• Personal demographics.

With this insight, marketers can create more targeted – and effective – campaigns.

Healthcare

Clustering is used in healthcare services to group patients based on their medical history, symptoms, or treatments. This can help doctors and other medical professionals identify patterns, set benchmarks, and make better diagnoses.

Finance

Clustering is used in finance to group stocks based on their performance, risk level, and other key metrics. This information can then be used to make investment decisions.

Social media

Clustering is also used by social media platforms to group users based on their behaviours or interests. This data can then be leveraged to create targeted advertising campaigns, or to recommend content to users.

Learn more about data clustering and analysis

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