Hierarchical clustering algorithm optimization method based on multi-core calculation

Abstract

The invention discloses a hierarchical clustering algorithm optimization method based on multi-core calculation, and relates to the technical field of parallelization, and the method comprises the steps: S1, preparing a compressed distance matrix storage space, and compressing the distance matrix storage space of a hierarchical clustering algorithm into an array da; S2, carrying out block numbering on the array da; S3, constructing a thread pool and a task queue; S4, putting the blocks into a task queue; S5, judging whether a task block exists in the task queue or not, if yes, entering S6, and if not, entering S8; S6, selecting a block, and calculating an index interval corresponding to the block in the original distance matrix; S7, according to the index interval indication area, calculating the distance between all sample points in the area, writing the distance back to the array da, and returning to S5; and S8, combining every two sample data by means of an index mapping relation, and ending clustering when the number of the combined categories is consistent with that of the to-be-processed sample data. The overall operation time of the hierarchical clustering algorithm is shortened, the calculation resource occupation is reduced, the operation efficiency is improved, and the format and size of input and output data are not affected.

Description

technical field [0001] The invention relates to the technical field of parallelization, in particular to a hierarchical clustering algorithm optimization method based on multi-core computing. Background technique [0002] Hierarchical clustering algorithm is an unsupervised machine learning algorithm that is widely used in various application fields. It classifies similar objects into the same cluster. The clustering process does not depend on pre-defined classes or training with class labels. Instances, labels are automatically determined by the clustering process. [0003] However, when analyzing a large amount of high-dimensional data through hierarchical clustering algorithms, it is difficult to overcome the problems of slow analysis speed and high computing resource usage. In some cases where analysis results need to be obtained quickly, it is necessary for the analysis algorithm to have a faster analysis speed. Obviously, the existing hierarchical clustering algorith...

AI Technical Summary

Problems solved by technology

Obviously, the existing hierarchical clustering algorithm is difficult to meet the requirements

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