An Efficient Density-based Clustering Algorithm for Higher-Dimensional Data

Thapana Boonchoo; Xiang Ao; Qing He

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arXiv · 1801.06965

An Efficient Density-based Clustering Algorithm for Higher-Dimensional Data

Thapana Boonchoo·Xiang Ao·Qing He

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Abstract

DBSCAN is a typically used clustering algorithm due to its clustering ability for arbitrarily-shaped clusters and its robustness to outliers. Generally, the complexity of DBSCAN is O(n^2) in the worst case, and it practically becomes more severe in higher dimension. Grid-based DBSCAN is one of the recent improved algorithms aiming at facilitating efficiency. However, the performance of grid-based DBSCAN still suffers from two problems: neighbour explosion and redundancies in merging, which make the algorithms infeasible in high-dimensional space. In this paper, we propose a novel algorithm named GDPAM attempting to extend Grid-based DBSCAN to higher data dimension. In GDPAM, a bitmap indexing is utilized to manage non-empty grids so that the neighbour grid queries can be performed efficiently. Furthermore, we adopt an efficient union-find algorithm to maintain the clustering information in order to reduce redundancies in the merging. The experimental results on both real-world and synthetic datasets demonstrate that the proposed algorithm outperforms the state-of-the-art exact/approximate DBSCAN and suggests a good scalability.

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Thapana Boonchoo, Xiang Ao, Qing He. 2018-01-22. An Efficient Density-based Clustering Algorithm for Higher-Dimensional Data. https://arxiv.org/abs/1801.06965

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