Intelligent multi-thread clustering method and device and computer readable storage medium

Abstract

The invention relates to an artificial intelligence technology, and discloses an intelligent multi-thread clustering method, which comprises the following steps of: receiving n data sample sets and aclustering number K input by a user, randomly determining K cluster centers according to the clustering number K, randomly dividing the n data sample sets into K blocks, and inputting the K blocks into K data modules; reading the sample sets in the K data modules by K threads, calculating the loss values of the K cluster centers and the n data sample sets, and judging the size relationship betweenthe loss values and a preset threshold value; and when the loss value is greater than the preset threshold value, re-determining the K cluster centers, re-calculating the loss value and judging the size relationship with the preset threshold value, and when the loss value is less than the preset threshold value, outputting the K cluster centers to complete a clustering result. The invention further provides an intelligent multi-thread clustering device and a computer readable storage medium. According to the invention, an accurate intelligent multi-thread clustering function can be realized.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a method, device and computer-readable storage medium for intelligent multi-thread clustering based on multiple sets of input data. Background technique [0002] Clustering is an important technology in information retrieval and data mining, and it is an effective means to analyze data and find useful information from it. It groups data objects into multiple classes or clusters, so that objects in the same cluster have a high degree of similarity, while objects in different clusters are very different. The Kmeans algorithm is one of the most commonly used and typical clustering algorithms, which is simple and easy to deploy, and is usually used as the first choice for large sample clustering analysis. The traditional Kmeans algorithm generally uses a single thread on the host side and a single GPU process on the device side when the amount of clustering d...

AI Technical Summary

Problems solved by technology

The traditional Kmeans algorithm generally uses a single thread on the host side and a single GPU process on the device side when the amount of clustering data is not large, but the operating efficiency is low in the case of massive data calculations , cannot converge quickly, and the calculation takes a long time, which limits the application of the Kmeans algorithm to a certain extent

Images