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.

A generalized classification and coding system of Human Disease Animal Model Resource data with a case study to show improving database retrieval efficiency

“…According to the first dimension of classification-the human disease [1], animal models can be classified into the following major categories: (1) cardiovascular system models, (2) digestive system models, (3) respiratory system models, (4) urinary system models, (5) reproductive system models, (6) endocrine diseases models, (7)ophthalmology and otolaryngology models, (8) oral diseases models, (9) bone diseases models, (10) skin diseases models, (11) nervous system models, (12) blood system models, (13) infectious diseases models, (14) tumor models, (15) traditional Chinese medicine (TCM) viscera dialectics models, and (16) animal models for other diseases. These categories can be further subdivided into 118 intermediate categories, which can further be subcategorized into more minor categories.…”

“…In the face of such difficulties, fortunately, the development of data processing technology itself has provided a powerful means for data management of animal models. Considering the characteristics of data such as multi-source, heterogeneity and imbalance, embedded multi-function data mining technology based on granular computing has been developed [ 14 ]. In order to use time series health data sets for selective prediction, researchers developed an algorithm using long short-term memory and unit-wise batch standardization [ 15 ].…”