Block Clustering

TL;DR

Divides the data space into a grid of blocks and assigns each data point to the block containing it to reveal clusters.
Helps identify clusters and outliers in large datasets while remaining computationally efficient and easy to interpret.
Less suitable for non-uniform data distributions and for capturing complex patterns beyond simple spatial proximity.

Definition

Block clustering is a method of grouping data points into clusters based on their spatial proximity by dividing the data space into a grid of blocks and assigning each data point to the block that it falls into.

Explanation

Block clustering works by partitioning the data space into a regular grid of blocks. Each data point is assigned to the block that contains it; blocks with multiple points indicate localized concentrations, which are interpreted as clusters. This grid-based approach makes the method computationally efficient and scalable for large datasets and produces results that are straightforward to interpret. Because assignments are based on block membership, the method can also highlight points that fall outside dense blocks as potential outliers.

Limitations noted in the source include reduced suitability for datasets with non-uniform distributions—where a fixed grid may not reflect the true data density—and an overall simplicity that may prevent capturing more complex patterns and trends.

Examples

Restaurants in a city

Divide a city into a grid of blocks and assign each restaurant to the block it falls into. This identifies clusters of restaurants (for example, clusters of Italian restaurants downtown or fast food in the suburbs).

Crime data

Divide a city into a grid of blocks and assign each crime to the block in which it occurred. This identifies clusters of criminal activity and can inform policing strategies and resource allocation.

Outlier detection

If a restaurant falls outside the cluster of restaurants in a particular area, it may be considered an outlier and warrant further investigation.

Use cases

Crime analysis
Marketing
Public health

Notes or pitfalls

May not be suitable for datasets with non-uniform distributions because a fixed grid of blocks can misrepresent actual data density.
Relatively simplistic and may not capture more complex patterns and trends within the data.

Clustering
Outliers