Modified Hausdorff distance and diffusion equation evolution combined fast manifold Kmeans

Bingcheng Li

doi:10.1117/12.2664124

12 June 2023 Modified Hausdorff distance and diffusion equation evolution combined fast manifold Kmeans

Bingcheng Li

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 125381G (2023) https://doi.org/10.1117/12.2664124
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

K-Means is one of the most popular clustering techniques and has been successfully used in many data mining fields. K-Means is computationally expensive for high dimension and high quantity data and various techniques have been developed to reduce the computational cost. These techniques mainly involve improving implementation efficiency. In this paper, we proposed a Hausdorff distance and diffusion equation evolution combined technique to speed up K-Means through reducing number of data points in distance computation. Experiments show that for manifold data, the proposed method not only significantly reduces the computational cost, but also improve the clustering performance.

Citation Download Citation

Bingcheng Li "Modified Hausdorff distance and diffusion equation evolution combined fast manifold Kmeans", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 125381G (12 June 2023); https://doi.org/10.1117/12.2664124

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