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Another one for dimension reduction trains a subspace projection matrix to project original data space into some low-dimensional subspaces which have deep architecture, so that the low-dimensional codes would be learned.
Unfortunately, most effective visual features are at high-dimensional data space [2].
Thus, it is difficult for computers to process such a huge data.
It tries to represent the data by the linear combination of a small number of basic elements, and the combination coefficients will be used as low dimensional data.
Kim: Data Mining and Knowledge Discovery, Vol. 26 (2013) No.3, p. 512-32 [3] J.Y.

According to reduction-factors of shear strength indexes, cohesion C and angle of internal friction j, SRM is classified into two types, single reduction-factor method that reduction-factors of the two indexes are same and two reduction-factors method that reduction-factors are different.
The computed results of 9 different reduction conditions by FLAC SRM and SRSM are listed in table 1, where the 6th reduction condition is just single reduction-factor method, and errors mean the difference of two reduction-factors obtained by FLAC SRM and SRSM.
Table 1 Reduction conditions and reduction-factors computed by FLAC SRM and SRSM Number Reduction condition Two reductoin-factors Error [%] Single reduction-factor FLAC SRM SRSM 1 Only C is reduceed, i.e the 1st pattern.
As shown in table 1, these can be seen as follows: (1) under the different conditions, values of two reduction-factors of C and j are not completely equal obtained by either of FLAC SRM and SRSM, so safety margin of the two indexes should not be same; (2) values of two reduction-factors obtained by FLAC SRM and SRSM are rather close, small errors in which maximal data is only 2.04%; (3) two reduction-factors under the different conditions have regularity that with increase of n, SRF1 goes up, SRF2 goes down, and increase rate of SRF1 is more than decrease rate of SRF2; (4) all single reduction-factors F are more than the lowest safety factor Fmin=1.259 which is the value of the 6th reduction condition, and this illustrates that the every two reduction-factors condition is not the most dangerous in single reduction-factor case;(5) single reduction-factor method is only a special case among different reduction methods.
(4) Which reduction condition of two reduction-factors method is suitable for a specific slope?

· Extracting knowledge from data.
Roadmap on technologies of model order reduction, data-sciences and hybridation.
The first concerns data analysis and more particularly data-reduction.
By combining both, physics-based models, calibrated by using data assimilation, and operating in real time by using advanced model order reduction techniques, with a data-driven model for describing the gap between the measures and the physics-based model prediction.
Model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction.

The evaluation from the data objects based on key attributes can reduce the data size and algorithm complexity.
After Clustering analysis of customers, then the evaluation analysis will process to the clustering data.
There are a lot of uncertain data of customer cluster, so the traditional method of classification and evaluation to the incomplete data is very difficult.
Tested by actual data analysis, cluster analysis can reduce the size of customer data and data noise, and the key class evaluation analysis can improve the evaluation efficiency of e-commerce customers.
Data Analysis Approaches Of Soft Sets Under Incomplete Information.

Without reactive power data, the system assumes that the power factor is fixed; without voltage data, the system makes use of the rated voltage.
In addition, with GPRS module installed in it, the meter can achieve wireless data transmission.
Therefore, decision support system for comprehensive power loss reduction of rural power network is designed with a common data interface.
In the process of data input and theoretical line loss calculation, the system can obtain existing data from other software systems avoiding manual input.
It ensures the accuracy of the data and improves efficiency of inputting data.

According to the distribution of SMEs in Ningbo, Our group hands out 1200 questionnaires in total and collected back 1035, 957 of which are valid except the questionnaires whose data are clearly abnormal.
By means of analyzing and comparing information and data which has been collected, our group find out the current SMEs’ actual needs and the problems existed in implementation of relevant policy during the process of energy conservation and emission reduction.
The Main Features of Energy Conservation and Emission Reduction of SMEs in Ningbo 3.1 Basic Situation of SMEs Surveyed Table 1 Industry Distribution of SMEs Surveyed Industry Distributed (A) Machinery &Industrial Products (B ) Clothing & Textile (C) Hardware & Tools (D) House- hold appliances (E) Auto Parts & Accessories (F) Building Materials (G) Office Equipment & Supplies (H) Service (I) Others 13.99% 20.99% 12.39% 7.45% 6.65% 11.01% 7.11% 8.03% 11.93% From: Data of Survey 3.2 Situation of Energy Conservation and Emission Reduction of SMEs Surveyed 3.2.1 High cost and low utilization rate of the SMEs’ energy utilization Table 2 Energy Cost of SMEs Surveyed Energy cost /Total cost of production More than 50% 30-50% 15-30% Less than 15% 5.83% 26.66% 48.33% 19.16% From: Data of Survey As is shown from the above table, the energy cost of the SMEs is high and utilization rate of the energy is low.
Compared with the number of SMEs and their employees, valid sample size of the survey 957 is not enough, as well as the objectivity of some responses to the questionnaire. 3.Limited statistical analysis of the survey data.
Due to the limitations of our relevant professional skills, our group failed to dig out the deeper problems hidden behind the data.

For the mixture, the experimental TG-DSC curves of mixture are compared with the calculated weighted sum, which is the sum of the TGA-DSC data of pure iron ores and biomass according to their weighting percentages, shown in Fig. 2 (b).
It is clear that the experimental data of the mixture is almost consistent with the calculated weight sum before 450 oC, mainly relating to the remove of the water combined in the iron ores.
Thereby, we could conclude that the other components in the iron ores have somewhat effect on the pyrolysis of the biomass, which leads to the difference between the experimental data and the calculated results.
The reduction of goethite ores was conducted in a lab-scale reactor.
Valix, Reduction roasting of limonite ores: effect of dehydroxylation, Int.

The data of water also shown in this figure.
The data show that coefficient of friction fibers at Re > 25.000 is lower that water data and Blasius equation.
Drag reduction occured if the data is higher than curve A.
The data is greater that curve A.
After Reynolds number about 35.000, the data shows constant.

In this paper the process of direct reduction of vanadium slag was adopted.
The test results showed that the regression equation curve fitting of the experiment data was very significant, the main factors affecting the vanadium slag reduction (according to the primary and secondary order) was the content of anhydrous sodium carbonate, roasting temperature, roasting time and reduction of carbon content.
The reduction process of iron oxides is a crystallization of chemical reaction.
In the table the baking reduction temperature was 1150℃, baking reduction time was 4h, the mass percentage of anhydrous sodium carbonate and slag was 5.56%.
Therefore the reduction temperature under the optimum condition was 1100℃.

But compare with these "massive" data, the ability of people to analyze the data and acquire knowledge exist a considerable gap, formed a "data glut" and "information poor" in a passive state.
Wisdom: Effective use of knowledge Meta-knowledge: Knowledge of the rules Knowledge: The rules of the use of information Information: Potentially useful of knowledge Data: Potentially useful information Noise: No obvious information Meta-knowledge Wisdom Knowledge Information Data Noise Fig.1 Hierarchy structure of knowledge In the hierarchy of knowledge, the lowest level (layer 6) is a noise which is almost made up of meaningless by the obscure nature of the data issues and the composition.
Level 5 is the data, some potentially significant issues.
Layer 4 is the information, is the result of a significant data processed.
The minimal attribute reduction sets is the serial code for {2,4,6,7,9,10,11,13,14}, TCF names are {Data communication, The importance of the system, Online data Processing, Multiple screens and multiple operations, Complex input and output, Complex internal processing, Code reusability, The perfect functionality and performance, Easy to maintain and modify}.