How can you identify data clusters using visualization?

1 What are data clusters?

Data clusters are collections of data points that are closer to each other than to other points in the same data set. Clustering is a form of unsupervised learning, which means that you do not have predefined labels or categories for your data, but you want to discover them based on the data characteristics. Data clusters can help you segment your data, identify outliers, find patterns, and simplify your analysis.

2 Why use visualization for clustering?

Visualization is an invaluable tool for exploring and communicating high-dimensional or complex data sets. It can help you to observe the overall distribution and shape of your data, detect potential clusters and outliers, compare different clustering methods and results, validate and refine your clusters, and interpret and explain your clusters.

3 How to prepare your data for clustering?

Before you can visualize your data clusters, you need to prepare your data for clustering by cleaning and transforming it to remove noise, missing values, and outliers. Additionally, you must choose the right variables and features to cluster your data, scale and normalize them to make them comparable and avoid bias, as well as reduce the dimensionality of your data to make them easier to visualize and cluster.

4 How to choose a clustering method?

Clustering methods can vary in their assumptions, advantages, and limitations. K-means, for example, partitions data into k clusters based on distance to the centroids, but you must specify the number of clusters in advance and it works best with spherical and well-separated clusters. Hierarchical clustering builds a tree-like structure of clusters based on similarity or dissimilarity between data points, with an option to choose between agglomerative (bottom-up) or divisive (top-down) approaches. Density-based clustering is another option; it groups data points based on the density of their neighborhoods without needing to specify the number of clusters, and can handle any shape or size of clusters as well as outliers.

5 How to visualize your clusters?

Depending on the clustering method and the dimensionality of your data, you can use different types of visualizations to display your clusters. Scatter plots, for instance, show the relationship between two or more variables and can use color, shape, or size to indicate cluster membership. Dendrograms are useful for hierarchical clustering and for choosing the optimal number of clusters, as they show the hierarchical structure of clusters as a tree. Heatmaps are ideal for high-dimensional data and for showing similarities and differences within and between clusters, as they show the values of each variable or feature for each data point as a matrix of colored cells.

6 How to evaluate and improve your clusters?

After you visualize your clusters, you may want to evaluate and improve them to ensure that they are meaningful and reliable. To do this, you can use internal or external validation measures, such as the silhouette coefficient or Davies-Bouldin index, to assess the quality and consistency of your clusters. Additionally, you can leverage domain knowledge and context to interpret and explain your clusters. Furthermore, interactive and exploratory visualization tools can be used to modify and refine your clusters by changing the number of clusters, the clustering method, or the variables or features.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?