基于角距离的高维DBSCAN学习加速器框架
Learned Accelerator Framework for Angular-Distance-Based High-Dimensional DBSCAN
摘要：
基于密度的聚类是数据科学中常用的工具。今天，许多数据科学工作都在利用高维神经嵌入。然而，传统的基于密度的聚类技术（如DBSCAN）在高维数据上的性能有所下降。在本文中，我们提出了LAF，一个通用的学习加速器框架，以加速原始DBSCAN和基于采样的DBSCAN的变体在高维数据与角距离度量。该框架包括一个学习基数估计器和一个后处理模块。基数估计器可以快速预测数据点是否为核心，以跳过不必要的范围查询，而后处理模块检测假阴性预测并合并错误分离的聚类。评估表明，我们的LAF增强的DBSCAN方法在效率和质量上都优于最先进的高效DBSCAN变体。
英文摘要：
Density-based clustering is a commonly used tool in data science. Today many data science works are utilizing high-dimensional neural embeddings. However, traditional density-based clustering techniques like DBSCAN have a degraded performance on high-dimensional data. In this paper, we propose LAF, a generic learned accelerator framework to speed up the original DBSCAN and the sampling-based variants of DBSCAN on high-dimensional data with angular distance metric. This framework consists of a learned cardinality estimator and a post-processing module. The cardinality estimator can fast predict whether a data point is core or not to skip unnecessary range queries, while the post-processing module detects the false negative predictions and merges the falsely separated clusters. The evaluation shows our LAF-enhanced DBSCAN method outperforms the state-of-the-art efficient DBSCAN variants on both efficiency and quality.
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