ClusterTools

ClusterTools is a software toolkit designed to simplify deploying and running clustering algorithms on compute clusters and distributed systems. It provides a modular architecture that unifies resource management, task scheduling, data handling, and algorithm execution to enable scalable clustering workflows.

Core components include a cluster execution engine, a plugin-based clustering library, a resource adapter layer that interfaces with common schedulers and orchestrators, and client interfaces such as a command line tool and a programming API. It supports integration with schedulers like SLURM and Torque, as well as container platforms such as Kubernetes, enabling batch and streaming processing.

Clustering methods are exposed through a plugin system, allowing users to implement or swap algorithms such as k-means, hierarchical, DBSCAN, spectral, and Gaussian mixture models. The framework supports multiple distance metrics and data representations, and can handle data that does not fit in memory via data partitioning or out-of-core processing.

Data handling and reproducibility are emphasized: preprocessing pipelines, result collection, and experiment cataloging with parameter sweeps and provenance metadata. Fault tolerance and checkpointing aid recovery from node failures during long-running jobs.

Typical use cases include bioinformatics, market and document clustering, image analysis, and anomaly detection in large-scale sensor data. ClusterTools aims to provide a usable, extensible foundation for researchers and engineers to run, compare, and reproduce clustering experiments at scale.