d3-scale-cluster
A D3 scale that clusters continuous data into discrete groups using a 1D clustering algorithm, similar to quantile scales.
What is d3-scale-cluster?
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.
Overview
D3 scale that clusters data into discrete groups
Use Cases
Best For
- Creating choropleth maps with naturally clustered color scales
- Binning continuous data for histograms with meaningful breakpoints
- Generating legend thresholds based on data distribution patterns
- Offloading scale computation to web workers for large datasets
- Comparing clustering vs. quantile/quantize approaches in visualizations
- Implementing custom D3 scales with advanced data grouping logic
Not Ideal For
- Real-time visualizations with massive, dynamically updating datasets where performance is critical.
- Projects requiring simple equal-interval data bins without the complexity of clustering algorithms.
- Environments not built on D3.js, as it's tightly integrated with D3's scale ecosystem.
Pros & Cons
Pros
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.
Cons
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.
Frequently Asked Questions
Related Projects
Textures.jsTextures.js is a JavaScript library for creating SVG patterns
crossfilterFast n-dimensional filtering and grouping of records.
d3-annotationUse d3-annotation with built-in annotation types, or extend it to make custom annotations. It is made for d3-v4 in SVG.
d3-legendA reusable d3 legend component.
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