Effective implementation of hierarchical clustering

Verma, Mudita; Vijayarajan, V.; Sivashanmugam, G; Amali, Geraldine Bessie

doi:10.1088/1757-899x/263/4/042094

Cited by 1 publication

(1 citation statement)

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“…There are many implementations of hierarchical clustering [13,14], some of which are parallelized [15,16,17]. In this paper we will work with the agglomerative variant, which forms clusters from the bottom-up -i.e.…”

Section: Overview Of Algorithmmentioning

confidence: 99%

Identifying clusters on a discrete periodic lattice via machine learning

Law

2019

Computer Physics Communications

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Given the ubiquity of lattice models in physics, it is imperative for researchers to possess robust methods for quantifying clusters on the lattice -whether they be Ising spins or clumps of molecules. Inspired by biophysical studies, we present Python code for handling clusters on a 2D periodic lattice. Properties of individual clusters, such as their area, can be obtained with a few function calls. Our code invokes an unsupervised machine learning method called hierarchical clustering, which is simultaneously effective for the present problem and simple enough for non-experts to grasp qualitatively. Moreover, our code transparently merges clusters neighboring each other across periodic boundaries using breadth-first search (BFS), an algorithm well-documented in computer science pedagogy. The fact that our code is written in Python -instead of proprietary languages -further enhances its value for reproducible science. PROGRAM SUMMARYProgram Title: Cluster Collector Licensing provisions: Creative Common by 4.0 Programming language: Python Nature of problem: Lattice simulations of, say, membrane proteins model the spatiotemporal organization of a system. In order to extract insights from such sim- ulations, we need robust methods for identifying clusters of simulated objects on the lattice. Solution method: Hierarchical clustering first identifies all potential clusters. Then, breadth-first search connects together clusters that neighbor each other across periodic boundaries.

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Section: Overview Of Algorithmmentioning

confidence: 99%

Identifying clusters on a discrete periodic lattice via machine learning

Law

2019

Computer Physics Communications

View full text Add to dashboard Cite

show abstract

Effective implementation of hierarchical clustering

Cited by 1 publication

References 2 publications

Identifying clusters on a discrete periodic lattice via machine learning

Identifying clusters on a discrete periodic lattice via machine learning

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