Multi-GPU Density Peak Clustering Method Based on Locality Sensitive Hashing

Abstract

The invention belongs to the field of data mining, aiming at solving the problems of too high computational complexity and too high time consumption when the existing density peak clustering method solves the clustering problem of large-scale and high-dimensional data sets, it provides a method based on local sensitive hashing. Multi-GPU density peak clustering method, including four processes: calculating distance matrix; calculating local density; calculating distance δ; calculating cluster centers and assigning clusters. The core idea is that the original data is divided by locality-sensitive hashing, and the shared memory is used to improve the reading and writing speed. This method makes full use of locality-sensitive hashing to divide similar data into the same GPU, which can reduce unnecessary distance calculations. In addition, multiple hash functions are designed to divide data through OR operations, reducing similar data being mapped to different GPUs, and implementing multi-GPU statistical methods based on the message passing interface MPI, which improves the parameter calculation speed.

Description

technical field [0001] The invention belongs to the field of data mining, in particular to a multi-GPU (Graphic Processing Unit, graphics processor) density peak clustering method based on local sensitive hashing. Background technique [0002] Clustering is an unsupervised classification technique, the purpose of which is to divide unlabeled data sets into limited categories or clusters according to the similarity between the data, and finally make the data similarity within the group large and the difference between the groups large. Because clustering can find hidden patterns in data sets, it has been widely used in many scientific researches such as machine learning, computer vision and bioinformatics. At present, there are mainly the following clustering methods: (1) K-means and K-medoids methods, which use the center of the data point as the corresponding clustering center, can only find the shortcomings of spherical clusters, and are not suitable for non-spherical clus...

AI Technical Summary

Problems solved by technology

[0010] The present invention will aim at solving the problem that the existing density peak clustering method is too high in computational complexity and time consumption when solving large-scale and high-dimensional data set clustering problems, and provides a multi-GPU density peak based on local sensitive hashing clustering method

Images