# Parallel D2-Clustering: Large-Scale Clustering of Discrete Distributions

Source: [Parallel D2-Clustering: Large-Scale Clustering of Discrete Distributions](<https://www.sota2.com/research/paper/68fa60d81e209131ce9f1595>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:47:33.519Z

Authors: Yu Zhang, James Z. Wang, Jia Li

Published: 2013-02-02T22:56:26.000Z

## Abstract

The discrete distribution clustering algorithm, namely D2-clustering, has demonstrated its usefulness in image classification and annotation where each object is represented by a bag of weighed vectors. The high computational complexity of the algorithm, however, limits its applications to large-scale problems. We present a parallel D2-clustering algorithm with substantially improved scalability. A hierarchical structure for parallel computing is devised to achieve a balance between the individual-node computation and the integration process of the algorithm. Additionally, it is shown that even with a single CPU, the hierarchical structure results in significant speed-up. Experiments on real-world large-scale image data, Youtube video data, and protein sequence data demonstrate the efficiency and wide applicability of the parallel D2-clustering algorithm. The loss in clustering accuracy is minor in comparison with the original sequential algorithm.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1302.0435>)

[PDF](<https://arxiv.org/pdf/1302.0435.pdf>)