Learned Accelerator Framework for Angular-Distance-Based High-Dimensional DBSCAN

Autor:	Wang, Yifan, Wang, Daisy Zhe
Rok vydání:	2023
Předmět:	Computer Science - Information Retrieval Computer Science - Databases Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by EDBT 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2302.03136 Zobrazit plný text záznamu View this record from Arxiv