Classification rule mining method under cloud computing environment

Abstract

The invention discloses a classification rule mining method under a cloud computing environment. The classification rule mining method is characterized in that a master-slave type organization structure consisting of a control center and a plurality of slave servers is adopted, and comprises the following steps of: dividing, by the control center, a data set to be classified into a training sample and a test sample, and distributing each of data blocks of the same size which are formed by uniformly dividing the training sample to one processing unit; training, by the processing units, the data blocks, by using a genetic algorithm to obtain an atomic rule for classification; and finally, reducing the atomic rule by a classifier, and selecting a reduction result which meets classification accuracy requirement as a final result of the classification rule mining. The classification rule mining method is suitable for data classification on distributed data storage under the cloud computing environment; distributed parallel processing of data classification tasks under the cloud computing environment can be performed; and the classification rule mining method has a positive effect on the classification processing problem of mass data under the cloud computing environment.

Description

technical field [0001] The invention belongs to the technical field of data analysis in a cloud computing environment, and in particular relates to a classification rule mining method in a cloud computing environment. Background technique [0002] Classification technology research is an important research field of data analysis and management in cloud computing environment. On the one hand, classification is an important task type of data mining. The data in the cloud computing environment has the characteristics of massiveness, distribution and dynamics. These characteristics bring challenges to data management in the cloud computing environment. The analysis of these data will help improve the efficiency of massive data analysis and management in the cloud computing environment. On the other hand, the cloud environment has ultra-large-scale storage and computing capabilities, dynamic scalability of resources and structures, and provides services on demand through virtual...

AI Technical Summary

Problems solved by technology

However, these methods still have bottleneck problems, including the need to scan and sort the data sets multiple times, resulting in inefficient algorithms; they are sensitive to noise and real data, and are prone to overfitting; the scalability of large training sets is not good. good wait

Especially in the cloud computing environment, the large-scale and dynamic nature of distributed massive data sets lead to a sharp increase in the search space and dimensions of the data classification process, which increases the computational complexity of classification and reduces the efficiency of traditional classification methods. The mining method of classification rules cannot be directly applied in the cloud computing environment

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