DivIntent

DivIntent is a Python library designed to facilitate the creation and analysis of divisive clustering algorithms, particularly those based on the divisive hierarchical clustering (DHC) approach. Introduced as an open-source tool, DivIntent aims to simplify the implementation and experimentation with various divisive clustering methods, which recursively partition datasets into smaller subsets.

The library provides a modular framework that allows users to implement custom splitting criteria or leverage existing ones, such as those based on statistical measures like variance, entropy, or domain-specific metrics. By abstracting away low-level implementation details, DivIntent enables researchers and practitioners to focus on algorithmic innovation rather than boilerplate code.

DivIntent supports both batch and incremental clustering, making it adaptable to different data scenarios, including static and streaming datasets. It integrates with popular Python data science libraries such as NumPy, SciPy, and scikit-learn, ensuring compatibility with existing workflows. The library also includes utilities for evaluating clustering performance using metrics like silhouette score, Davies-Bouldin index, or custom metrics.

Developed with extensibility in mind, DivIntent encourages contributions from the community, allowing for the addition of new splitting strategies, optimizations, or integrations with other tools. While primarily focused on clustering, its modular design may also inspire applications in related fields like anomaly detection or hierarchical data organization.

DivIntent is distributed under an open-source license, promoting collaborative development and accessibility. It is particularly useful for academic research, prototyping new clustering algorithms, or educational purposes where understanding divisive methods is key. Documentation and example implementations are provided to assist users in getting started with the library.