CommonNNClustering

CommonNNClustering implements density-based clustering in Python using the common-nearest-neighbor (CommonNN) method to identify clusters of arbitrary shapes and sizes and to robustly detect outliers for applications such as molecular dynamics conformational analysis and kinetic (core-set) Markov-state model construction.

Key Features:

• Density-Based Clustering: Implements the common-nearest-neighbor (CommonNN) density-based clustering method to detect clusters of arbitrary shapes and sizes and to provide robustness against outliers.
• Hierarchical Clustering: Provides hierarchical clustering functionality to explore cluster structure at multiple levels of granularity.
• Evaluation Capabilities: Includes tools for evaluating clustering results to assess cluster quality and reliability.
• Molecular Dynamics Application: Applies clustering to identify low-energetic molecular conformations relevant for constructing kinetic (core-set) Markov-state models.
• Python Implementation: Provided as a Python package to integrate into scientific workflows and computational analyses.

Scientific Applications:

• Molecular Dynamics Conformational Analysis: Identification and grouping of low-energy molecular conformations for downstream analysis.
• Kinetic (Core-Set) Markov-State Model Construction: Support for extracting conformational states used in building kinetic Markov-state models.
• General Data Science Clustering: Detection of complex cluster structures and outlier-robust clustering across diverse datasets.

Methodology:

Reimplementation of the common-nearest-neighbor (CommonNN) density-based clustering method; includes hierarchical clustering routines and clustering evaluation tools and the implementation prioritizes computational efficiency.