Fast global K-means clustering method of using OpenCL (Open Computing Language) acceleration

Abstract

The invention discloses a fast global K-means clustering method of using OpenCL (Open Computing Language) acceleration. The method includes the realization steps of: (1) reading in a data set and a total clustering number; (2) transposing data at an OpenCL hardware equipment end; (3) calculating a centroid, and using the same as a first initial clustering center point; (4) using a K-means algorithm for clustering; (5) selecting a new initial clustering center point at the OpenCL hardware equipment end; (6) judging whether a current total clustering center point number is less than or equal tothe total clustering number, if yes, executing the step (4), and otherwise, executing a step (7); and (7) outputting a clustering result. The method can realize real-time processing on massive clustering data on any hardware equipment supporting an open computing language.

Description

technical field [0001] The invention belongs to the technical field of data processing, and further relates to a Fastglobal K-means clustering method accelerated by OpenCL (Open Computing Language) hardware equipment in the technical field of data mining. The invention can realize the parallel accelerated fast global K-means clustering method, and can realize the real-time processing of massive data on the open computing language OpenCL hardware device. Background technique [0002] The fast global K-means algorithm uses a deterministic method that does not depend on any initial parameter value instead of random search to obtain the initial cluster center, which solves the problem of poor stability of the clustering results of the traditional K-means clustering method based on local search. And by optimizing the calculation method of global K-means to increase the new cluster center, the running time is reduced. [0003] TCL Group Co., Ltd. disclosed a patent document "A me...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the parallelized K-means code implemented by the Spark platform cannot be transplanted on various hardware devices that support the open computing language OpenCL, and the portability is not high. It is only suitable for distributed systems and cannot Parallel acceleration on GPU, FPGA, MIC and other hardware devices greatly limits its application scope

However, this method still has the disadvantage that it needs to store a large amount of intermediate calculation results when accelerating the algorithm on the heterogeneous device side. When the input image data is too large, it exceeds the storage capacity of the heterogeneous device, resulting in This method cannot realize the processing of larger image data

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