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The example of value reduction based on the improved algorithm illustrated that it is an effective value reduction algorithm and an important supplement of basic value reduction algorithm.
Introduction The task of knowledge discovery in database is to find the internal links among large amount of data and finally access to interesting decision-making rules.
[2] Chan P and Stolfo S: On the Accuracy of Meta-learning for Scalable Data Mining, Journal of Intelligent Systems, vol. 8, 1997 pp.5-28
[3] Anannd S: EDM: A General Framework for Data Mining Based on Evidence Theory, Data & Knowledge Engineering, vol. 18, 1996, pp.189-223
[4] Chen M.: Data Mining: An Overview from a Database Perspective, IEEE Transactions on Knowledge and Data Engineering, vol. 8, 1996, pp.866-883

The natural decrease in accuracy with the use of less data compared to the gain due to the reduction of experimental effort is analysed.
Using the vertices and symmetrical distribution of the data within the full experimental matrix allows a drastic reduction of experimental effort while maintaining the initial accuracy.
The data distribution for reduced data sets achieving the best accuracy can then be optimized inversely.
Fitting with reduced data For the fitting with reduced data two key questions have to be answered, 1) how much data are necessary to maintain the same accuracy as the initial fit and 2) how should the data be distributed within the full experimental matrix.
The material model imposes a variety of conditions on the data distribution.

In practical engineering, equipment condition data are usually produced incrementally, so how to minimize repeated reduction on the new data set has an important significance to improve the efficiency of the diagnosis algorithm.
The resolution matrix for data expansion.
By calculating the matrix and doing reduction, we can get the reduction result after data expansion: Table 1 : Reduction results after data expansion Reduction results after data expansion Data type Changes of U1 and U2 Changes of M1 and M2 Reduction results 1) R(U)={C4,C7,C10} 2) R(U)={C3,C8,C9} 3) R(U)={C4,C7,C10} 4) R(U)={C3,C7,C10} In the examples after attributes added, here, we mainly extracted the following 4 frequency domain characteristics, which are expressed as P1, P2, P3 and P4 [7]: ,,, (4) In the formulas , is spectrum , K represent for the number of spectral lines , and K=1，2，…K .
What’s more, with the reduction results and bearing data, we can get the bearing running states and can better monitor the bearing workings.
Dang, Attribute reduction for dynamic data sets, Applied Soft Computing. 13 (2013) 676–689

According to the results of simulation, it shows good agreement with experimental data.
From the data in the figure, it can be seen in different operating points the error between simulation result and experimental data is very small, generally within 5%.
Comparison of simulation results with experimental data.
Comparison of simulation results with experimental data.
All these simulation results compared with experimental data shows good agreement.

Make Huangbai criterion and sample data as weibull distribution to calculate similar.
Information reduction method description Information reduction method is based on rough set[3, 4].
Rough set theory can be regarded as a new mathematical tool for imperfect data analysis.
Rough set based data analysis starts from a data table called a decision table, columns of which are labeled by attributes, rows---by objects of interest and entries of the table are attribute values.
After calculated, the fuzzy centre data of recommended samples are(0.0694±0.0731, 0.1158±0.3044, 0.0131±0.0144); the fuzzy centre data of no-recommended samples are(0.1684±0.0983, 0.0318±0.0177, 0.0077±0.0083).

Based on the statistical data of Changsha-Zhuzhou-Xiangtan (CZT) urban agglomeration from the year 2005 to 2010, the environmental learning curves of sulfur dioxide emission per 10 thousand Yuan (RMB) production value and per capita gross domestic product (GDP) were established, and sulfur dioxide emission reduction potential of these cities was analyzed.
Data Source.
All data used in this paper came from Statistical Yearbook of Hunan Province, Changsha Statistical Yearbook, Zhuzhou Statistical Yearbook, Xiangtan Statistical Yearbook and the web site of Hunan Provincial Statistics Bureau.
From the data, it also can be seen that the decrease of sulfur dioxide emission per 10 thousand Yuan production value in Changsha city is the largest, and then in Zhuzhou city.
Sulfur dioxide emission reduction potential.

In this work, the RSM, simulated in FORTRAN, is validated with hot rolling experimental data for higher reductions.
The modified model was simulated and validated with hot rolling experimental data for hot rolling schedules at low strain rates and low reductions (<10%).
The modified model was then simulated in FORTRAN and validated with hot rolling experimental data for different hot rolling schedules at high reductions (up to 22.7%).
The required input data were rolling speed, furnace temperature, initial and final height of the specimen, and specimen width.
Results and Discussion The output of the FORTRAN codes shows temperature data for AISI316 specimen with different geometrical forms.

Basic Attribute Reduction Algorithm Based on Discriminability Matrix Algorithm Description The general steps of attribute reduction based on discriminability matrix are: firstly obtain the core of attribute reduction set with discriminability matrix and then to calculate attribute reduction set with reduction algorithm.
Improved Attribute Reduction Algorithm The minimum reduction is same as others reduction of an information system, which are both NP complex problems.
References [1] Chan P and Stolfo S. : On the Accuracy of Meta-learning for Scalable Data Mining, Journal of Intelligent Systems, vol. 8, 1997 pp.5-28
[2] Anannd S. : EDM: A General Framework for Data Mining Based on Evidence Theory, Data & Knowledge Engineering, vol. 18, 1996, pp.189-223
[5] Chen M: Data Mining: An Overview from a Database Perspective, IEEE Transactions on Knowledge and Data Engineering, vol. 8, 1996, pp.866-883.

Relevant literatures proposed electricity energy-saving key technical support system architecture, such as energy consumption, pollutant emissions supervision and inspection and data analysis support system, energy saving and emission reduction targets, evaluation and assessment technical support system[5].
Since the specific condition for each area is different, emission capacity can be calculated by substituting corresponding data into the simplified model.
Our data is mainly from Jilin Grid Inc.
To calculate the capacity for 2011, the data we need is as following: coal power for 2011’s Jilin is 5.684×1010kWh, or 1.7×107t for coal consumption, wind power is 3.968×109kWh, or 1.19×106t for coal consumption.
Limited by data, we can only calculate operational emission reduction capability for Jilin grid taking one year as the unit time.

Analyze and conclude the relationship data between existing-pipeline-controlled rainfall return period and runoff coefficient, by using source control.
Calculate and conclude the design scale of ecological measures and storage pools, draw the corresponding generalized model, by using peak flow reduction, volume reduction and flow reduction measures.
That includes existing sewer discharge return period improving, peak flow reduction, volume reduction, flow reduction etc.
Fig5 The melts generally model of flood volume reduction Determine the scale of flowing reduction mode Flowing reduction measures include stormwater detention zone, vegetation swales, storage pool etc.
Fig.7 The schematic picture of flood rate reduction Conclusion This paper presents several measures of runoff reduction, which includes existing sewer discharge return period improving, peak flow reduction, volume reduction, flow reduction etc. and analyzes the handling facilities scale of these various measures in Zhangjiakou city, which is selected as a study area.