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ADvaNCE – Efficient and Scalable Approximate Density-Based Clustering Based on Hashing
Volume 28, Issue 1 (2017), pp. 105–130
Pub. online: 1 January 2017      Type: Research Article      Open accessOpen Access
Received
1 September 2016
Accepted
1 February 2017
Published
1 January 2017

Abstract

Analysing massive amounts of data and extracting value from it has become key across different disciplines. As the amounts of data grow rapidly, current approaches for data analysis are no longer efficient. This is particularly true for clustering algorithms where distance calculations between pairs of points dominate overall time: the more data points are in the dataset, the bigger the share of time needed for distance calculations.
Crucial to the data analysis and clustering process, however, is that it is rarely straightforward: instead, parameters need to be determined and tuned first. Entirely accurate results are thus rarely needed and instead we can sacrifice little precision of the final result to accelerate the computation. In this paper we develop ADvaNCE, a new approach based on approximating DBSCAN. More specifically, we propose two measures to reduce distance calculation overhead and to consequently approximate DBSCAN: (1) locality sensitive hashing to approximate and speed up distance calculations and (2) representative point selection to reduce the number of distance calculations.
The experiments show that the resulting clustering algorithm is more scalable than the state-of-the-art as the datasets become bigger. Compared with the most recent approximation technique for DBSCAN, our approach is in general one order of magnitude faster (at most 30× in our experiments) as the size of the datasets increase.

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Biographies

Li Tianrun

T. Li is currently pursuing a PhD in databases and data mining at the University of Wisconsin.

Heinis Thomas
t.heinis@imperial.ac.uk

T. Heinis is currently a lecturer at Imperial College London. The research of his group focuses on the topics around data management in general and scientific databases, high-performance data analytics as well as approximate computing in particular.

Luk Wayne

W. Luk is a full-time professor at Imperial College London. He leads the Custom Computing Research Group at Imperial focusing on research of reconfigurable hardware. He is a fellow of the Royal Academy of Engineering, IEEE and the British Computer Society. He is also a senior advisor of Maxeler and a Honorary Fellowship Advisor of the Croucher Foundation.


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Keywords
analytics clustering high-dimensional data approximate computing

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