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In order to solve the problems of low accuracy of data mining, high relative error rate of evaluation, and long time of evaluation in traditional government debt risk evaluation methods, this paper proposes a modeling method of government debt risk comprehensive evaluation based on multidimensional data mining. The MAFIA algorithm is used for multidimensional mining of government debt risk data, and K-means clustering algorithm is used for clustering processing of mined data. The KMV model is built based on the clustering findings, and the uncertainty factor is utilized to alter the model in order to provide a complete assessment of government debt risk using the modified KMV model. The experimental results show that the accuracy rate of government debt risk data mining is always above 91%, the relative error rate of evaluation is always below 3.4%, and the average evaluation time is 0.71 s, the practical application effect is good. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low security coefficient and low storage efficiency of traditional methods, a power information security partition storage method based on multidimensional data mining is designed. Firstly, the relationship value between power information data is analyzed and determined, and the power information collection is completed with the help of covariance matrix. Then, the membership function of multidimensional power information data is calculated, and the noise reduction of multidimensional power information is completed by calculating Lagrange coefficient. Finally, the multidimensional information data is analyzed by two-dimensional correlation, the multidimensional power information data is layered, the partition structure is optimized, the data of the three regions after stratification are encrypted respectively, so as to complete the secure storage of power information data. Experimental results show that the security factor of power information security partition storage using this method is always higher than 0.9, and the storage efficiency is high. [ABSTRACT FROM AUTHOR]

Bile acids (BAs), as crucial endogenous metabolites, are closely related to cholestasis, metabolic disorders, and cancer. To better understand their function and disease pathogenesis, global profiling of BAs is necessary. Here, multidimensional data mining was developed for the discovery and identification of potentially unknown BAs in cholestasis rats. Based on an in-house theoretical BA database and using a newly established liquid chromatography-tandem high-resolution mass spectrometry (LC-HRMS/MS) method, four-dimensional (4D) data including the retention times (RT), abundances, HRMS, and HRMS/MS spectra were acquired and elucidated. And 491 BAs were totally profiled. Then, the relationships between RT with different conjugation types, different positions and configurations of hydroxyl/ketone groups as well as fragmentation rules of hydroxyl, ortho-hydroxyl, ketone, and conjugated groups of BAs were summarized to assist BA identification for the first time. Finally, 292 BAs were assigned with molecular formulas, 201 of which were putatively identified by integrating the 4D data, applying structure-driven relative retention time rules, and a comparison with synthetic BAs. The estimated concentrations of 201 BAs, including 93 reported and 108 newly identified BAs, were quantified by using surrogate standards with similar structure. Among 201 BAs, 38 BAs were detected in both humans and rats for the first time. Our strategy has expanded the scope of BAs and provides a way to identify a class of metabolites. Compared to normal rats, the significantly increased sulfated and glucuronide conjugated BAs in urine and feces from experimentally cholestatic rats may reveal a way to diagnose intrahepatic cholestasis. Image 1 • An in-house BA database and novel UHPLC-MS/MS method were established for global profiling of 491 BAs in rats. • Multidimensional data mining was developed to putatively identify 201 BAs in bio-samples. • The change in sulfo-BAs and glucu-BAs suggested new ways to diagnose and treat BA metabolic disorders. [ABSTRACT FROM AUTHOR]

For the problems such as mining low accuracy of algorithm, low speed and large memory footprint when digging the complex and large-scale data sets in the biological information network, this paper proposed a biological information network multi-dimensional data mining algorithm that based on association rules mapping. The algorithm combined association mapping relationship between the network dataset to determine the association rules of network dataset, and introduced the mining factor and relative error to improve mining accuracy of the algorithm. According to the multi-dimensional subspace degree of association between the data sets to distinguish the subspace and subspace datasets in order to achieve effective excavation of different data sets. The experiment al results on the memory usage of the algorithm on the number of different sets of data, the accuracy of mining algorithm, the simulation of algorithm running time, show the association rule mining algorithm can effectively improve the mining map accuracy, reduce the memory footprint and enhance the computing speed. [ABSTRACT FROM AUTHOR]