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The knowledge discovery process has the following steps: data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation, knowledge representation.
The data mining module according to the model library system model, the data warehouse multidimensional data sets for data mining, in order to obtain the required Knowledge.
On physical education comprehensive quality index by, through data mining based on rough set attribute reduction algorithm.
The C2 attribute significance degree is 0, so do not select C2 joined the reduction set, i.e. the last attribute reduction set is Red = { Ratio of height and weight, Fitness function } Insect.
Advanced Scout: Data Mining and Knowledge Discovery in NBA Data.

To correct the reduction modes, it is necessary to have experimental data on the geometry and wall thickness deviation ends of the roughing tubes after the continuous mill.
Some data in relation to TPA-80, having in its composition 8-stand continuous and 24-stand reduction mills, published earlier [5,6,7].
Processing of Initial Data For improving the technological modes of rolling in order to reduce the length of the thickened ends, at the first stage, the analysis and processing of actual data on the end wall thickness deviation were carried out.
However, an analysis of the actual data showed that Eq.1 gives understated values of thickening due to the fact that it was obtained for reduction in one stand and does not take into account the features of the TPA-80 multistand reduction mill.
A distinctive feature of the technique is the calculation based on actual data on the volume of the end thickening, which must be reduced by a specified value when rolling the end areas of pipes in the head stands of the reduction mill.

Data from UNEP (United Nations Environment Program) Roise Center disclosed, 100 developing countries took part in CDM, meaning 91% Kyoto Protocol of non Annex 1 countries involved in emission reduction.
The averaged RERs amount of all CDM projects in each host country, data came from UNEP Roise Center.
Investment data was collected from the CDM Project Design Document ( PDD), data posted on UNEP Roise Center.
Data was from the World Economic Forum “World Competitiveness Report ( 2010)”.
Data was from World Bank open database (2010).

After searching and screening, get in line with the requirements of the research on data integration [4-12], which contains the CH4 emission data of 51, distributed in 7 test points.
The rice planting area of Nanjing, Zhenjiang, Changzhou, Wuxi, Suzhou rice planting area in yearbook data were searched.
In this study, the rice average CH4 emissions of the foundation data were calculated for the study area CH4 fluxes by using SPSS software.
Results and Analysis CH4 emission reduction potential.
Although the potential reduction per unit area was the smallest in suzhou, but the total emissions reduction potential was 15.51%, the total emissions reduction potential was the smallest instead of wuxi, due to wuxi rice planting area was smallest.

Oxide impurity inhibited the reduction kinetics of the ilmenite, yielding a lower reduction extent[1].Suresh’s study showed that reduction rate decreased with increasing weathering degree of ilmenite[2].
Temperature had great influence on the reduction rate.
Fig. 3 Effect of temperature on the solid state reduction degree of ilmenite To determine the solid reduction mechanism of ilmenite, shrinking unreacted core model was used to analyze the isothermal reduction results.
Experimental data at the interface was used as follow: where x is fractional reduction, t is time and k is rate constant; The plot of curve-fitting was shown in Fig.4.
Temperature had great influence on the reduction rate.

On the above basis, an attribute reduction algorithm is presented.
This kind of decision table with definite form has been widely used in data mining, machinery learning, decision analysis, process control, data analysis, artificial intelligence, and so on[6-12].
Yang, “Dominance-based rough set approach to incomplete interval-valued information system,” International Journal of Data & Knowledge Engineering, vol. 68, no. 11, pp. 1331-1347, 2009 [7] H.
Masamoto, “Application of data mining to quantitative structure-activity relationship using rough set theory,” International Journal of Chemometrics and Intelligent Laboratory Systems, vol. 99, no. 1, pp. 66-70, 2009 [8] N.
Gerhard, “Adapted variable precision rough set approach for EEG analysis,” International Journal of Artificial Intelligence in Medicine, vol. 47, no. 3, pp. 239-261, 2009 [9] Tuan-Fang Fan, Churn-Jung Liau, Duen-Ren Liu, “A relational perspective of attribute reduction in rough set-based data analysis,” European Journal of Operational Research, Vol.213, no.1, pp. 270-278,2011 [10] Yuhua Qian, Jiye Liang, Witold Pedrycz, “An efficient accelerator for attribute reduction from incomplete data in rough set framework,” Pattern Recognition, Vol.44, no.8, pp.1658-1670,2011 [11] Qiang He, Congxin Wu, Degang Chen, Suyun Zhao, “Fuzzy rough set based attribute reduction for information systems with fuzzy decisions,” Knowledge-Based Systems, Vol.24, no.5, pp. 689-696,2011 [12] Yan-Qing Yao, Ju-Sheng Mi, Zhou-Jun Li, “Attribute reduction based on generalized fuzzy evidence theory in fuzzy decision systems,” Fuzzy Sets and Systems, Vo.170,no.1 pp. 64-75,2011 [13] Lifeng Li, Jianke Zhang, “Attribute

The time was calculated by equation (7) with the data of mass loss percentage, and its relationship between -ln(1-fc) is shown in Fig.3.
The relationship of time and 1-(1-f)1/3 was obtained by substitution of the data of mass loss fraction, shown in Fig5.
The relationship of time and 1-2/3f-(1-f)2/3 was obtained by substitution of the data of mass loss fraction, shown in Fig7.
The activation energy was calculated by the data from Fig.4, Fig 6 and Fig 8, while the controlling step was different, shown in Table 2.
If it was, the was 50-75kJ/mol, which was different from the experimental data.

Spectrum Analysis of Cell Resistance Data Sources.
The data sources, which this paper studies, are based on the electrolytic cell control system in 306KA.
And the sampling items adopted in electric-hydraulic control system simulation are data without abnormal step disturbance, removing artificial factors.
Data Pre-processing.
Here the original data takes away the average number of all the analyzing cell resistance to reduce the zero frequency influence.

This article conducted an empirical study with China’s provincial data in an attempt to test that whether China’s industrial restructuring may have an effect on emission reduction.
We would need more micro data for that purpose.
This article would use this database and collect provincial data from four-digit businesses.
This article has processed the data as follows: (1) deleted the data from mining industry, power industry, gas industry and water industry, only reserve the data from manufacturing industry; (2) rearranged the data before 2003 according to national economy industry classification(GB/T 4754-002); (3) filled up the missing data (such as added value, sales volume, gross output, output of new products, export value and other variables) in 2004 with the average value of the data from the year 2003 and 2004; (4) only reserved four-digit businesses that had complete data during 1998 to 2007.This article drew from the constructing method of indexes measuring the changes in government spending put forward by Brender & Drazen (2009), and constructed the following indexes to measure the extent of industrial restructuring: This construction is similar to that of Liu Kai’s (2007), and is called Michaeli index
All the other data of emissions come from the journal China Environment Yearbook.

Therefore, the reduction rate could be expressed as Eqn.4
Figure 1 shows a comparison between the experimental data and the calculated data.
So it can be used to forecast the reduction process and analyze the reduction mechanism.
Fig. 3 Influence of pellet porosity on reduction rate Fig. 4 Influence of pellet radius on reduction rate Figure 4 shows that the radius has significant effect on the reduction rate, under the furnace temperature at 1373K.
When the reduction time is 23 minutes and the radius varies from 7mm to 13mm, the reduction rate changes from 99% to 78%.