A D3 scale that clusters continuous data into discrete groups using a 1D clustering algorithm, similar to quantile scales.
d3-scale-cluster is a custom D3.js scale that clusters continuous numeric data into discrete groups using a 1-dimensional clustering algorithm. It solves the problem of creating more intuitive data groupings for visualizations like choropleth maps, where traditional quantile or quantize scales might produce misleading breaks.
Data visualization developers using D3.js who need to create color scales or data bins based on natural clustering of values, particularly for thematic mapping or histogram-like displays.
Developers choose d3-scale-cluster because it provides more meaningful data groupings than D3's built-in quantile/quantize scales by identifying natural breaks in data distributions, leading to clearer and more accurate visual representations.
D3 scale that clusters data into discrete groups
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Uses the Ckmeans algorithm to identify intuitive clusters in data, leading to more accurate visual groupings as shown in the choropleth comparison graphic.
Follows the standard D3 scale API with methods like domain(), range(), and invertExtent(), making it easy for D3 users to adopt without learning a new interface.
Includes import() and export() methods to offload computation to web workers, preventing main thread blocking for large datasets, as detailed in the README.
Documentation clearly states O(kn log(n)) runtime and provides examples for performance testing, helping developers assess suitability upfront.
The clustering algorithm's O(kn log(n)) runtime can be slow for very large datasets, and the README admits it's only fast enough for most cases, requiring performance testing.
It's tightly coupled with D3.js, so it cannot be used in projects that don't rely on D3, limiting its applicability to broader JavaScript environments.
Only supports 1-dimensional clustering, making it unsuitable for multi-dimensional data analysis that requires more complex grouping techniques.