This paper addresses the clustering of data in the hyperdimensional computing (HDC) domain. In prior work, an HDC-based clustering framework, referred to as HDCluster, has been proposed. However, the performance of the existing HDCluster is not robust. The performance of HDCluster is degraded as the hypervectors for the clusters are chosen at random during the initialization step. To overcome this bottleneck, we assign the initial cluster hypervectors by exploring the similarity of the encoded data, referred to as query hypervectors. Intra-cluster hypervectors have a higher similarity than inter-cluster hypervectors. Harnessing the similarity results among query hypervectors, this paper proposes four HDC-based clustering algorithms: similarity-based k-means, equal bin-width histogram, equal bin-height histogram, and similarity-based affinity propagation. Experimental results illustrate that: (i) Compared to the existing HDCluster, our proposed HDC-based clustering algorithms can achieve better accuracy, more robust performance, fewer iterations, and less execution time. Similarity-based affinity propagation outperforms the other three HDC-based clustering algorithms on eight datasets by 2% ~ 38% in clustering accuracy. (ii) Even for one-pass clustering, i.e., without any iterative update of the cluster hypervectors, our proposed algorithms can provide more robust clustering accuracy than HDCluster. (iii) Over eight datasets, five out of eight can achieve higher or comparable accuracy when projected onto the hyperdimensional space. Traditional clustering is more desirable than HDC when the number of clusters, $k$ , is large.

中文翻译：

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使用超维计算的鲁棒集群

本文解决了超维计算（HDC）领域中的数据聚类问题。在之前的工作中，已经提出了一种基于HDC的集群框架，称为HDCluster。然而，现有HDCluster的性能并不稳健。由于在初始化步骤中随机选择集群的超向量，HDCluster 的性能会下降。为了克服这个瓶颈，我们通过探索编码数据的相似性来分配初始聚类超向量，称为查询超向量。簇内超向量比簇间超向量具有更高的相似性。利用查询超向量之间的相似性结果，本文提出了四种基于 HDC 的聚类算法：基于相似性的 k 均值、等箱宽度直方图、等箱高度直方图和基于相似性的亲和力传播。实验结果表明：（i）与现有的 HDCluster 相比，我们提出的基于 HDC 的聚类算法可以实现更好的精度、更鲁棒的性能、更少的迭代和更少的执行时间。基于相似性的亲和力传播在八个数据集上的聚类精度优于其他三种基于 HDC 的聚类算法 2% ~ 38%。 (ii) 即使对于一次性聚类，即无需对聚类超向量进行任何迭代更新，我们提出的算法也可以提供比 HDCluster 更稳健的聚类精度。 (iii) 在八个数据集上，八个数据集中的五个在投影到超维空间时可以达到更高或相当的精度。当集群数量较多时，传统集群比 HDC 更可取， $k$ ，很大。