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Improvements for the approximate reduction method of rough set knowledge Rough set theory is a mathematical tool which is used to study imprecise, uncertain, incomplete data.
However, the quality of the data in the database has an impact on the completeness and accuracy of knowledge, so the initial data collection is essential to knowledge acquisition.
In this paper, we use the KDD method which is based on rough set data mining algorithms to acquire knowledge data, the process is shown in Figure 1.
Extract data from the database and express the data in the form of two-dimensional table, and construct an initial decision table; (3) Quantify condition attribute.
Introduction of Data and Knowledge Engineering[M].

The results predicted are in good agreement with the experiment data.
All the data were collected and saved with a computer.
Comparisons of experimental data and theoretical data of DR USL=1.0m/s Figure 9.
Comparisons of experimental data and theoretical data of DR Conclusions Adding a small amount of PAM has a little influence on the viscosity of aqueous solution.
The results predicted are in good agreement with the experiment data.

The deviation between SCM and CCM with the experimental data was attributed to porosity, thermodynamic properties and minute thermal fluctuations within the sample during the reduction process.
However, due to lack of information in term of thermodynamics data for TiOxCyNz, so the solid TiC and TiN are employed instead of TiOxCyNz.
However by increasing the reduction temperature, the deviations became smaller and the experimental data trended more closely to the predicted results.
Comparison of predicted and experimental extent of reduction of ilmenite at different temperatures Furthermore, the shape of the curves indicated chemical controlled reaction based on the observations of the data [10].
The reason behind this was due to the insufficient thermodynamic data available for TiOxCyNz.

If D is the distance matrix of a data matrix X, then .
As shown in the Fig.1 is the original data drawn in 3D space, which clearly show that the data can’t be separate from DLBCL to FL at all.
After being reduced the dimension, the low-dimensional data were classified by SVM, and Fig.2 shows the data after being reduced by MDS.
Dimensional-reduced data in 3D Space Figure 1.
Data Mining and Knowledge Discovery 2: 121–167

The normal parameter reduction in soft set is difficult to application in data mining because of great calculation quantity.
Experience has shown that the method is feasible and fast.. 1.Introduction The nature of the uncertainty data appearing in economics, engineering, environmental science, sociology, medical science, and many other fields with the complexity of uncertain data can be very different.
Parameter reduction is the key problem in soft set.
It would take much time to search the reduction.
In Section 3, normal parameter reduction in soft set is reviewed and the reduction model is given.

Good agreement is achieved between the predicted data and the experimental data.
Experimental method to obtain forming limit data Nakazima test is used to obtain the forming limit strains.
First, the region with most thickness reduction in forming process should be found.
Fig.5 Ubiety about calculated data and experimental FLC Fig.6 The ratio of experimental limit strains to calculated strains under different strain ratios Fig.7 Relationship between and strain ratio In region of tension-compression and tension- tension, and is used for adjustment respectively.
A new curve fitting method for forming limit experimental data.

The interpretation of physisorption data showed that the pore size after reduction with various CO concentration comprised microporous size in the range of 6-40 nm.
The first peak for three data has appeared at 392 °C (10% CO), 367 °C (20% CO) and 363 °C (40% CO) represented as peak I.
The data obtained through XRD showed that the chemical reaction starts occurring nearest to the temperature at 400 °C.
Under reduction at 700 °C (10% and 20%) of CO concentration, XRD data showed there is still a small peak of unreduced NiO left at 37.20° and 43.23°.
For identification purposes of crystalline phase composition, diffraction patterns obtained were matched with standard diffraction data (ICDD) files.

Evaluating the Reliability of Satellite Gravity Data for Disaster Risk Mapping in West Coast Aceh's Coal Mines Adib Adib1,a*, Radhiyullah Armi2,b, Sabrian Tri Anda2,c, Azwanda Azwanda2,d 1University of Teuku Umar, West Aceh, Indonesia 2University of Samudra, Langsa, Indonesia aadib@utu.ac.id, bradhiyullah@unsam.ac.id, csabriantrianda@unsam.ac.id, dazwanda@utu.ac.id Keywords: Satellite Gravity Data, Disaster Risk Reduction, FHD, SVD.
This integration aligns with recent advancements in geophysical data analysis, such as those discussed by Pohan et al. [8], which emphasize the importance of combining multiple data sources for robust risk assessment.
The spatial resolution for latitude and longitude is 1 minute per grid, with an accuracy of roughly 0.1 mGal for gravity data and 1 meter for elevation data.
Future efforts should focus on creating integrated geophysical models that combine multiple data sources to improve predictive capabilities and address emerging challenges in disaster risk reduction.
This access has been essential in enabling the research and data analyzing done.

For the data reduction two procedures were applied.
Data reduction procedures.
The data reduction procedure aimed obtaining Paris’ type relationship, Eq.1
For the purpose of comparison two variants of data reduction procedure were used.
Unfortunately, the data reduction method applied was not reported.

Application Choose compatibility data zoo and monks from UCI machine learning data, using the algorithm of reference[7] and this paper to calculate reduction of compatibility decision table, results as follows in table one.
Table one Compare with the reduction of compatibility decision table data Number of objects number of condition attributes Literature Algorithm[7] Algorithm number of condition attributes after reduction Run time number of condition attributes after reduction Run time zoo 101 16 14 0.032 5 0.026 monks 432 6 3 0.344 3 0.057 Choose incompatibility data ACLI and haves from UCI machine learning data, using the algorithm of reference[8] and this paper to calculate reduction of compatibility decision table, results as follows in table one.
Table two Compare with the reduction of incompatibility decision table data Number of objects number of condition attributes Literature Algorithm[8] Algorithm number of condition attributes after reduction Run time number of condition attributes after reduction Run time ACLI 140 6 6 0.812 6 0.314 hayes 132 4 3 0.567 3 0.219 Acknowledgement Tangshan Bureau of science and technology: 121302106b References [1] Guo-yin WANG.
Research on Algorithm of attribute reduction based on attribute significance.
Improvement on attribute reduction algorithm of discernibility matrix[J].