A Reference Point K-Nearest Neighbor Classification Method Based on MPI Parallelization

Abstract

The invention relates to a reference point k-nearest neighbor classification method based on MPI parallelization, and belongs to the field of data classification. The method includes steps: S1: The k-nearest neighbor algorithm based on the reference point uses the distance between the sample point and several reference points to measure the position difference between each other, defines the position difference factor, calculates the similarity between the training sample and the reference point, and generates an ordered S2: Search the approximate neighbor samples in the training set from the ordered sequence according to the similarity between the test sample and the reference point; S3: Calculate the exact similarity with the test sample from the searched approximate neighbor samples, so as to find k neighbor samples and judge the category. The present invention accelerates the search of k-nearest neighbors by means of reference points and uses MPI technology to implement parallelization, thereby accelerating the classification speed of large-scale and high-dimensional data.

Description

technical field [0001] The invention belongs to the field of data classification and relates to a reference point k nearest neighbor classification method based on MPI parallelization. Background technique [0002] Classification is an important technology in the field of data mining. Its purpose is to construct a classification model (also known as classification function, classifier) ​​according to the characteristics of the data set, which can map samples of unknown categories to certain categories in a given category. one or several. The k-nearest neighbor algorithm was originally proposed by Cover and Hart in 1968. It is a non-parametric classification technique that has the advantages of robustness, clear concept, and easy implementation, and can achieve high classification accuracy for unknown and non-normal distributions. Rate. [0003] The traditional k-nearest neighbor algorithm has a high time complexity. At present, many scholars have proposed many improved alg...

AI Technical Summary

Problems solved by technology

The disadvantage of this type of algorithm is that as the dimension of the data set increases, the performance of these tree-structured algorithms gradually deteriorates. The reason is that high-dimensional data sets will increase the complexity of the tree structure, which will lead to the establishment of tree structures and search for neighbors. Points and increased time taken to calculate distances

The kNN algorithm is a commonly used data mining algorithm, but its time complexity is high and the classification speed is slow.

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