A Novel DBSCAN Based on Binary Local Sensitive Hashing and Binary-KNN Representation

Abstract

We revisit the classic DBSCAN algorithm by proposing a series of strategies to improve its robustness to various densities and its efficiency. Unlike the original DBSCAN, we first use the binary local sensitive hashing (LSH) which enables faster region query for the k neighbors of a data point. The binary data representation method based on k neighborhood is then proposed to map the dataset into the Hamming space for faster cluster expansion. We define a core point based on binary influence space to enhance the robustness to various densities. Also, we propose a seed point selection method, which is based on influence space and k neighborhood similarity, to select some seed points instead of all the neighborhood during cluster expansion. Consequently, the number of region queries can be decreased. The experimental results show that the improved algorithm can greatly improve the clustering speed under the premise of ensuring better algorithm clustering accuracy, especially for large-scale datasets.

Theoretically-Efficient and Practical Parallel DBSCAN

“…DBSCAN * and H-DBSCAN * are variants of DBSCAN where only core points are included in clusters [22]. Other variants use approximate neighbor queries to speed up DBSCAN [50,89].…”