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首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >Analyzing Data Changes Using Mean Shift Clustering
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Analyzing Data Changes Using Mean Shift Clustering

机译：使用均值漂移聚类分析数据变化

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摘要

A nonparametric unsupervised method for analyzing changes in complex datasets is proposed. It is based on the mean shift clustering algorithm. Mean shift is used to cluster the old and new datasets and compare the results in a nonparametric manner. Each point from the new dataset naturally belongs to a cluster of points from its dataset. The method is also able to find to which cluster the point belongs in the old dataset and use this information to report qualitative differences between that dataset and the new one. Changes in local cluster distribution are also reported. The report can then be used to try to understand the underlying reasons which caused the changes in the distributions. On the basis of this method, a transductive transfer learning method for automatically labeling data from the new dataset is also proposed. This labeled data is used, in addition to the old training set, to train a classifier better suited to the new dataset. The algorithm has been implemented and tested on simulated and real (a stereo image pair) datasets. Its performance was also compared with several state-of-the-art methods.
机译：提出了一种用于分析复杂数据集变化的非参数无监督方法。它基于均值漂移聚类算法。均值平移用于对新旧数据集进行聚类，并以非参数方式比较结果。新数据集中的每个点自然都属于其数据集中的点集群。该方法还能够找到该点在旧数据集中属于哪个群集，并使用此信息报告该数据集与新数据集之间的质量差异。还报告了本地群集分布的变化。然后可以使用该报告来尝试了解导致分布变化的根本原因。在此方法的基础上，还提出了一种自动标记新数据集中数据的转导学习方法。除了旧的训练集之外，还使用此标记的数据来训练更适合新数据集的分类器。该算法已在模拟和真实（立体图像对）数据集上实现和测试。还将其性能与几种最先进的方法进行了比较。

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