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Data Validation.
Enhance review of the mining and exploration data by visual assessment of spatial data.
Some data anomalies are identified easily.
There is a substantial reduction in learning and assessment process time compared to conventional approaches.
Only some drilling data formats, ventilation network data formats and some logging data formats are similar to those of the international standards, the other data mining formats are different.

The data in the paper is complete, true and credible.
First, discretizing the data, and using Equal Frequency here.
Original data set Thresholding discretization Data pre -processing Format conversion Rough sets processing module Attribute reduction Training sample set Supporting vector machine regression module Choose kernel function Parameters optimization Test samples SVM regression model Output Fig 1 Model flow chart of Rough Set and Supporting Vector Machine Conclusions According to Table 2 the error in the support vector machine regression is less, we can conclude that using RSSVM to evaluate the sustainable development of regional power generation enterprise can get a better result.
From the feedback data of the questionnaire, we can clearly see that……the effect of sustainable development is notable.
The evaluation method is of certain reference for the comprehensive evaluation with less data sample and higher precision requirements.

In the storage they are responsible for grains heating which lead to losses of germination, discoloration, reduction of nutritional values and odor changes.
Antifungal activities of GNP against F. verticillioides on PDA at different concentrations (data are shown as average values and standard deviation of diameter fungal colony - each point represents an average of triplicate measurements).
Antifungal activities of GNP against P. citrinum on PDA at different concentrations (data are shown as average values and standard deviation of diameter fungal colony - each point represents an average of triplicate measurements).
Antifungal activities of GNP against A. flavus on PDA at different concentrations (data are shown as average values and standard deviation of diameter fungal colony - each point represents an average of triplicate measurements).
These SEM analyzes can confirm the rupture of the fungal cell membrane resulting in possible reduction of the enzymatic activity of the microorganism due to GNP presence.

K-means clustering based on feature weights optimizing K-means [9] clustering method cut data set containing n data points into k subsets, in which represents each data point contains m features.
is the distance of ith data point and jth data point, which can be calculated by the equation: (2) Constraint condition of Eq1 is as follow: (3) Eq3 shows that each data point of X belongs to only one class.
Test results and analysis We use fault test data of the case western reserve university bearing data center as the test data [10].
[10].The Case Western Reserve University Bearing Data Center.
Bearing data center fault test data[EB/OL]. http：//www.eecs.cwru.edu/laboratory/ bearing/ [2011-01-01]

Incomplete data set usually cause bias due to differences between observed and unobserved data.
A straightforward approach to deal with this problem is to ignore the missing data and to discard those incomplete cases from the data set.
Types of problems that are usually associated with missing values are [1] 1) loss of efficiency; 2) complications in handling and analyzing the data; 3) bias resulting from differences between missing and complete data (bias estimates) and; 4) reduction of statistical power (inefficient estimates).
The equation of coefficient of determination (R2) is given as follows [3]: (15) where N is the number of imputations, Oi is the observed data points, Pi is the imputed data point,is the average of imputed data, is the average of observed data, is the standard deviation of the imputed data and is the standard deviation of the observed data.
Since the data used is a real data set, comparison was made by fitting the Gamma distribution to the data.

Both methods are subjective,and the requirements for data of working conditions is huge.
Based on the discriminate analysis, classify the driving data.
According to discriminate analysis, test design and data acquisition would be purposeful, the traffic character would be presented by less data and the classification result of condition data would be obtained effectively.
Traffic condition data acquisition and characteristic parameters analysis 1 2 To get the data which can reflect actual traffic condition, a survey of Tianjin was conduct.
And data acquisition test could be designed more effectively to get the specific data.

This paper presents a hybrid K means clustering algorithm based on Principal Components Analysis(PCA) and Particle Swarm Optimization(PSO) to solve target recognition problems. the data of samples is processed by PCA and dimensionality reduction is done.
The step of dimension reduction is generally as following: (1) Standardize the original data and eliminate the influence of dimension
Data clustering problem is usually evaluate by the solution of tectonic target function.The function could be denoted by Eq.(5)
Dimensionality reduction has achieved and p eigenvectors form new characteristic sets:.
Rousseeuw: Finding groups in data: An introduction to cluster analysis(John Wiley & Sons, New York 1990)

Step2: reduction of attributes According to the definition of reduction and core, we can calculate unresolved relations of different attributes and reduction and core in the decision table.
However, it’s difficult to get data and compare with other objects, therefore, we don’t choose any index to assess international competitiveness.
resources: website of WTO; US Fiber Organ2008; China statistical yearbook 2009àinternational data in 2009; annual report 2009 on fiber and global market research paper on textiles & nonwoven industry and WERNER international textiles authentic research center.
We can get 4 reductions. ，，，.
As these 4 reductions are the same in meaning, so we choose to establish a new decision table.

This paper’s modifies and development based on Tapio model, analysis the energy consumption, carbon emissions and economic growth from the elasticity of factor decomposition, then the analysis and evaluation of the factors influence will be conduct, which contribute to the continued decoupling and choice of industrial low-carbon path of the powerful data support and advice.
The reference data for discharge coefficient of energy carbon refer the NDRC
According to the existing research results, constructed the following function: (7) (DEi=DEeg, DEcg, DEht; any of indexes are greater than zero) Example Analysis Data Preparation.
This study depends on the publication data from China Statistical Yearbook, China high-tech, Tianjin statistical yearbook issues 2000 and 2011.
Emissions influence power presents two grade, the first: 2001emissions decoupling influence is negative, in this phase the emission reduction implementation effect isn’t obvious; After 2001 decoupling influence tends to be stable, Tianjin’s emission reduction have been advancing steadily, but the influence is still weak.It shows that energy saving and emission reduction level of Tianjin is not good enough, lack of contribution.Compared with the former, industry cluster decoupling influence power was more stable and more contribution. 

We sort out the social responsibility of low-carbon performance indicators of the CNPC by the above figure, and enter the data based on the 2006-2011 CSR Report of CNPC, then give its weight according to the average method.
Table 2 The data of financial performance of CNPC (unit: %) 2006 2007 2008 2009 2010 2011 Net interest rate of total assets 0.1612 0.1552 0.1054 0.0733 0.0909 0.0761 Rate of return on net assets 0.2515 0.2158 0.1485 0.1171 0.1491 0.1396 Sales net profit rate 0.1606 0.1542 0.0989 0.0871 0.0875 0.07962 Empirical Analysis Data Source: Sample data about low-carbon performance of CNPC is derived from corporate social responsibility reports and annual reports of CNPC from 2006 to 2011.
Data about financial performance of CNPC is derived from finance and stock websites Sina and is arranged by us.
Research model: the data in Table 3 are based on Table 1 and Table 2.
Due to limited sources, only CSR data of CNPC from 2006 to June of 2011 are obtained; and there is a little sample data for research; more discussion will be focused on index weight to CSR and low-carbon performance data.