High-dimensional data clustering method, electronic device and system

Abstract

The invention provides a high-dimensional data clustering method, electronic device and system. The method comprises the following steps: obtaining the high-dimensional data to be clustered after dimension reduction and encryption from a plurality of clients; obtaining the corresponding base clustering by calculating the high-dimensional data to be clustered after dimension reduction and encryption; calculating the similarity between any two base clusters in the base clustering set; initializing the preset reference clustering; using the iterative algorithm to obtain candidate reference clusters successively based on the preset reference clusters and the tag sets corresponding to the base clusters. The preset reference clusters are updated iteratively until the preset reference clusters are no longer changed, if the preset reference clusters and candidate reference clusters have higher average similarity with the other clusters in the base cluster set, then the preset reference clusters are used as the final clustering results. The invention improves the accuracy of the high-dimensional data clustering result and controls the calculation amount.

Description

technical field [0001] The present invention relates to the technical field of the Internet, and more specifically, to a high-dimensional data clustering method, electronic equipment and a system. Background technique [0002] With the rapid development of Internet technology, a very large amount of data will be generated in real time every day, including mobile communication, electricity consumption data, network transactions and real-time monitoring information. The explosive growth of information volume makes data clustering one of the most important issues in the field of modern information management. [0003] The K-Means algorithm is the most classic distance-based clustering algorithm, which uses distance as the evaluation index of similarity, that is, the closer the distance between two objects is, the greater the similarity between the two objects will be. [0004] At present, when clustering data based on the K-means algorithm, the data to be classified is formed ...

AI Technical Summary

Problems solved by technology

[0005] However, for high-dimensional data, when using the K-means algorithm for clustering operations, the accuracy of the clustering results is low and the calculation is very heavy.

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