Clustering remains an important and challenging task of grouping samples into
clusters without manual annotations. Recent works have achieved excellent
results on small datasets by performing clustering on feature representations
learned from self-supervised learning. However, for datasets with a large
number of clusters, such as ImageNet, current methods still can not achieve
high clustering performance. In this paper, we propose Contrastive
Learning-based Clustering (CLC), which uses contrastive learning to directly
learn cluster assignment. We decompose the representation into two parts: one
encodes the categorical information under an equipartition constraint, and the
other captures the instance-wise factors. We propose a contrastive loss using
both parts of the representation. We theoretically analyze the proposed
contrastive loss and reveal that CLC sets different weights for the negative
samples while learning cluster assignments. Further gradient analysis shows
that the larger weights tend to focus more on the hard negative samples.
Therefore, the proposed loss has high expressiveness that enables us to
efficiently learn cluster assignments. Experimental evaluation shows that CLC
achieves overall state-of-the-art or highly competitive clustering performance
on multiple benchmark datasets. In particular, we achieve 53.4% accuracy on the
full ImageNet dataset and outperform existing methods by large margins (+
10.2%).

本文提出了一种基于对比学习的聚类方法，通过对表示进行分解，并使用分别编码不同信息的部分构建对比损失函数，可以高效地学习聚类结果。实验结果表明，该方法在多个标准数据集上取得了最新的甚至高居领先的聚类性能，尤其在 ImageNet 数据集上的准确率达到了 53.4％，并且优于现有方法。

基于对比表示学习的聚类分配

CLC: Cluster Assignment via Contrastive Representation Learning

Clustering high-dimensional spatiotemporal data using an unsupervised
approach is a challenging problem for many data-driven applications. Existing
state-of-the-art methods for unsupervised clustering use different similarity
and distance functions but focus on either spatial or temporal features of the
data. Concentrating on joint deep representation learning of spatial and
temporal features, we propose Deep Spatiotemporal Clustering (DSC), a novel
algorithm for the temporal clustering of high-dimensional spatiotemporal data
using an unsupervised deep learning method. Inspired by the U-net architecture,
DSC utilizes an autoencoder integrating CNN-RNN layers to learn latent
representations of the spatiotemporal data. DSC also includes a unique layer
for cluster assignment on latent representations that uses the Student's
t-distribution. By optimizing the clustering loss and data reconstruction loss
simultaneously, the algorithm gradually improves clustering assignments and the
nonlinear mapping between low-dimensional latent feature space and
high-dimensional original data space. A multivariate spatiotemporal climate
dataset is used to evaluate the efficacy of the proposed method. Our extensive
experiments show our approach outperforms both conventional and deep
learning-based unsupervised clustering algorithms. Additionally, we compared
the proposed model with its various variants (CNN encoder, CNN autoencoder,
CNN-RNN encoder, CNN-RNN autoencoder, etc.) to get insight into using both the
CNN and RNN layers in the autoencoder, and our proposed technique outperforms
these variants in terms of clustering results.

利用 DSC 算法，结合 CNN-RNN 模型学习数据的空间和时间特征，通过聚类和数据重构误差优化来实现高维时空数据的无监督聚类，并在气候数据上的实验结果表明，该方法的效果要优于现有的无监督聚类算法。

深度时空聚类：一种针对多维气候数据的时间聚类方法

Deep Spatiotemporal Clustering: A Temporal Clustering Approach for  Multi-dimensional Climate Data

Deep clustering outperforms conventional clustering by mutually promoting
representation learning and cluster assignment. However, most existing deep
clustering methods suffer from two major drawbacks. First, most cluster
assignment methods are based on simple distance comparison and highly dependent
on the target distribution generated by a handcrafted nonlinear mapping. These
facts largely limit the possible performance that deep clustering methods can
reach. Second, the clustering results can be easily guided towards wrong
direction by the misassigned samples in each cluster. The existing deep
clustering methods are incapable of discriminating such samples. To address
these issues, a novel modular Self-Evolutionary Clustering (Self-EvoC)
framework is constructed, which boosts the clustering performance by
classification in a self-supervised manner. Fuzzy theory is used to score the
sample membership with probability which evaluates the intermediate clustering
result certainty of each sample. Based on which, the most reliable samples can
be selected and augmented. The augmented data are employed to fine-tune an
off-the-shelf deep network classifier with the labels from the clustering,
which results in a model to generate the target distribution. The proposed
framework can efficiently discriminate sample outliers and generate better
target distribution with the assistance of self-supervised classifier.
Extensive experiments indicate that the Self-EvoC remarkably outperforms
state-of-the-art deep clustering methods on three benchmark datasets.

提出 Self-Evolutionary Clustering (Self-EvoC) 框架，采用模糊理论识别异常数据点并采用自我监督分类器生成目标分布以实现分类。实验证明，该方法优于现有 deep clustering 方法。

自我进化聚类

Self-Evolutionary Clustering

Many clustering algorithms exist that estimate a cluster centroid, such as
K-means, K-medoids or mean-shift, but no algorithm seems to exist that clusters
data by returning exactly K meaningful modes. We propose a natural definition
of a K-modes objective function by combining the notions of density and cluster
assignment. The algorithm becomes K-means and K-medoids in the limit of very
large and very small scales. Computationally, it is slightly slower than
K-means but much faster than mean-shift or K-medoids. Unlike K-means, it is
able to find centroids that are valid patterns, truly representative of a
cluster, even with nonconvex clusters, and appears robust to outliers and
misspecification of the scale and number of clusters.

通过密度和簇分配的概念，提出了一种 K-modes 目标函数算法，能够有效地聚类数据并找到有效的模式，相比于 K-medoids 和 mean-shift 更快且更加鲁棒。