Research on Embedded Multifunctional Data Mining Technology Based on Granular Computing

Abstract

Due to the influence and limitations of the multisourced, heterogeneous, and unbalanced characteristics of embedded multifunctional data, the application effect of the current data mining technology is not good, and the accuracy is low. To solve the above problems, an embedded multifunctional data mining technology based on granular computing was studied. According to the three characteristics of embedded multifunctional data, preprocessing such as data reduction, data standardization, and data balance were implemented. We implemented data granulation for the preprocessed data and calculated the data granulation characteristics, including offset, particle density, and intraparticle interval. Taking granular features as the input content, embedded multifunctional data mining was realized by using a neural network to complete the objectives of data classification, anomaly detection, fault identification, and so on. The experimental results showed that the anomaly mining results of each type of data mining were greater than 0.9, indicating that the accuracy of the mining technology is high.