基于场合的模糊谱划分集成

Xiaolong Meng, Yan Yang, Hongjun Wang
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引用次数: 0

摘要

聚类集成利用集成学习技术,将多个聚类成员的结果组合在一起,得到统一合理的聚类结果。本文对光谱划分集成算法的阶段结果进行有序整合,在光谱划分集成的后续聚类阶段应用模糊c均值聚类算法,并根据不同场合提出了四种模糊光谱划分集成。与现有的图划分集成算法相比,我们的算法在聚类效果和效率上都有所提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy Spectral Partition Ensemble Based on Occasion
Clustering ensemble takes advantage of ensemble learning technique, combining multiple cluster members' results to get uniform and more reasonable clustering result. This paper integrates in the staged results of spectral partition ensemble algorithm orderly, applying the fuzzy C-means clustering algorithm in the following clustering stage of spectral partition ensemble, and presents four fuzzy spectral partition ensemble based on occasion. Compared with the existing graph partition ensemble algorithms, our algorithms do better in the clustering effectiveness and efficiency.
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