并行核聚类方法工具箱

10th International Conference on Pattern Recognition Systems (ICPRS-2019) Pub Date : 1900-01-01 DOI:10.1049/cp.2019.0253

S. Mouysset, R. Guivarch

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引用次数: 0

摘要

许多领域，如生物学、信息检索、图像分割等，都需要能够在没有先验形状或位置信息的情况下收集数据的无监督方法。通过研究一种基于重叠域分解的并行策略，我们提出了一个工具箱，该工具箱是两种完全无监督核方法的并行实现，分别基于基于密度的性质和基于谱的性质，以处理模式识别领域的大数据集。

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ParKerC: Toolbox for Parallel Kernel Clustering Methods

A large variety of ﬁelds such as biology, information retrieval, image segmentation needs unsupervised methods able to gather data without a priori information on shapes or locality. By investigating a parallel strategy based on overlapping domain decomposition, we present a toolbox which is a parallel implementation of two fully unsupervised kernel methods respectively based on density-based properties and spectral properties in order to treat large data sets in ﬁelds of pattern recognition.

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10th International Conference on Pattern Recognition Systems (ICPRS-2019)

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