We present a novel approach, in which we learn to cluster data directly from
side information, in the form of a small set of pairwise examples. Unlike
previous methods, with or without side information, we do not need to know the
number of clusters, their centers or any kind of distance metric for
similarity. Our method is able to divide the same data points in various ways
dependant on the needs of a specific task, defined by the side information.
Contrastingly, other work generally finds only the intrinsic, most obvious,
clusters. Inspired by the mean shift algorithm, we implement our new clustering
approach using a custom iterative neural network to create Differentiable Mean
Shift (DMS), a state of the art, dataset agnostic, clustering method. We found
that it was possible to train a strong cluster definition without enforcing a
constraint that each cluster must be presented during training. DMS outperforms
current methods in both the intrinsic and non-intrinsic dataset tasks.

该研究提出了一种基于侧面信息学习数据聚类的新方法，使用自定义迭代神经网络实现 Differentiable Mean Shift（DMS）进行聚类，该方法不需要知道聚类数量、中心点或任何距离度量，可在不同任务需要的情况下将相同的数据点分成不同的类别，且 DMS 在固有和非固有数据集任务中表现优异。