Chen, Yewang, Tang, Shenyu, Bouguila, Nizar, Wang, Cheng, Du, Jixiang and Li, HaiLin
(2018)
*A Fast Clustering Algorithm based on pruning unnecessary distance computations in DBSCAN for High-Dimensional Data.*
Pattern Recognition
.
ISSN 00313203
(In Press)

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Official URL: http://dx.doi.org/10.1016/j.patcog.2018.05.030

## Abstract

Clustering is an important technique to deal with large scale data which are explosively created in internet. Most data are high-dimensional with a lot of noise, which brings great challenges to retrieval, classification and understanding. No current existing approach is “optimal” for large scale data. For example, DBSCAN requires O(n2) time, Fast-DBSCAN only works well in 2 dimensions, and ρ-Approximate DBSCAN runs in O(n) expected time which needs dimension D to be a relative small constant for the linear running time to hold. However, we prove theoretically and experimentally that ρ-Approximate DBSCAN degenerates to an O(n2) algorithm in very high dimension such that 2D > > n. In this paper, we propose a novel local neighborhood searching technique, and apply it to improve DBSCAN, named as NQ-DBSCAN, such that a large number of unnecessary distance computations can be effectively reduced. Theoretical analysis and experimental results show that NQ-DBSCAN averagely runs in O(n*log(n)) with the help of indexing technique, and the best case is O(n) if proper parameters are used, which makes it suitable for many realtime data.

Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering |
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Item Type: | Article |

Refereed: | Yes |

Authors: | Chen, Yewang and Tang, Shenyu and Bouguila, Nizar and Wang, Cheng and Du, Jixiang and Li, HaiLin |

Journal or Publication: | Pattern Recognition |

Date: | 5 June 2018 |

Digital Object Identifier (DOI): | 10.1016/j.patcog.2018.05.030 |

Keywords: | DBSCAN ρ-Approximate DBSCANNQ-DBSCAN |

ID Code: | 983946 |

Deposited By: | Monique Lane |

Deposited On: | 14 Jun 2018 19:19 |

Last Modified: | 05 Jun 2020 00:00 |

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