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Because the LDA topics exists uncertainty distribution and rough set can deal with uncertain data well, so the algorithm based on rough set can improve the accuracy of topics analysis.
Rough set doesn’t need membership functions and to know much background about the data of given problem.
Data collection and LDA topic mining We collect data from www.sohu.com, www.sina.com and other websites as training and test data, and the contents cover computers, education, sports, entertainment, technology and automotive.
Since LDA topical mining is incomplete, and rough set for dealing with non-precision data has better adaptability, a topic reduction algorithm is proposed based on rough set.
Mining Incomplete Data—A Rough Set Approach[M]//Emerging Paradigms in Machine Learning.

The results of current reversal chronopotentiometry and thermodynamic data showed that both the silicon deposition and the side reaction between SiO2 and magnesium result in the loss of magnesium and low current efficiency.
It indicates that the reduction process is diffusion-controlled.
Square wave voltammogram of SiO2 reduction on a tungsten electrode.
As is shown in Fig. 5, the electrode potential slowly drifts towards more negative values, and the electrode potential suddenly jumps to more negative values corresponds to the reduction of Mg(� ) ions after a well-defined transition time τ.�� The data plotted in Fig. 6 agree with the following equation[6]: 1/2 1/2 / 4 1/2 ln RT t E E nF t τ τ = +
Chronopotentiogram of SiO2 reduction on a tungsten electrode.

Reuse SVM to signal sequences data continuation to improve EEMD.
Improved EEMD threshold noise reduction steps.
Fig.3 is the noise reduction error after four methods.
EEMD in threshold noise reduction using white noise is effectively restrain the noise, the noise reduction result is better.
Advances in Adaptive Data Analysis, Vol.1(2009),p1-41 [8] Chongfeng Cao, Shixi Yang and Jianxin Yang.

The study objective is to simplify test data, reduction and analysis which lead to significant time saving.
Typical PPMT curve to obtain engineering parameters Pencel Pressuremeter Testing and Data Reduction Following equipment saturation, the calibrations are performed.
Raw and reduced data with calibrations applied Data Acquisition Hardware Improvement The study aim is to simplify data collection, reduction and analysis of the test data used for calibrations, testing and determining the subsequent engineering parameters.
Accuracy of the Collected Data.
Using the digital implementation and APMT software, additional time is saved, including time taken for data collection, data reduction and determination of engineering parameters.

To attribute reduction in an uncertain information system, this paper proposed a method of attribute reduction based on rough set theory.
Introduction Rough set theory [1,2] was put forward by Prof Pawlak who was a Polish mathematician in 1980s, which is a tool to deal with uncertainty and vagueness of data.
In information systems with massive data sets, due to huge number of attributes and examples, the attribute reduction algorithm efficiency is particularly important[3,4].
Rough Sets and Intelligent Data Analysis[J].
An algorithm for attributes reduction.

Definition 4: Data sheet, C is condition attributes set, a set of all the necessary attributes in C is called the core of C, written as Core(C), the core of data sheet is public part of all reduction.
The attributes reduction of continuous fault data The diesel engine vibration data about large maintenance machinery of the decision Table shown in Table 2 shown to illustrate.
Rough Sets: Theoretical aspects of reasoning about data[M].
Rough Sets and intelligent data analysis[J].
Principle and Algorithm of Data Mining[M].

Simplification of decision tables has been investigated by many authors, the current attribute reduction methods include data analysis, discernibility matrix, information entropy [4,5], etc.
References [1] Pawlak Z: Rough sets and intelligent data analysis, Information Sciences (2002), p. 147:1-12
[3] Pawlak Z: Rough sets: Theoretical Aspects of Reasoning about Data, Warsaw (1991), p. x
[4] Yuqing Peng, GuoXi Xiao, and Xin Yang: Data Structure Algorithm Animation Demo implementation of CAI software, Journal of Continue Education of Hebei University of Technology, vol. 15(Mar. 2000), p.1-4
[9] Pawlak Z: Rough sets: Theoretical Aspects of Reasoning about Data, Warsaw (1991), p. 60

Energy conservation and emission reduction have become important issues people concern.
Table1 Current situation of energy consumption for key industries in Jilin Province Year Industry 2006 2007 2008 2009 2010 Farm and sideline food processing industry 2.62 2.25 1.99 1.70 1.14 Raw chemical materials and chemicals manufa- -cturing industry 4.48 1.47 1．62 1.20 1.04 Electricity and heating power production and supply industry 32.96 28.20 29.00 18.31 16.23 Data source: calculated from Statistical Yearbook of Jilin Province from 2006-2010 and Statistical Database for China’s Economic Development Table2 Current situation of SO2 (Ton) emission for key industries in Jilin Province Industry Year 2005 2006 2007 2008 2009 Thermal power industry 171361.77 220157.8 236382.08 186382.4 192122.6 Electricity and heating power production and supply industry 188518.09 243246.78 277980.68 210295.04 217607.09 Data source: first draft of “the 12th five-year” plans for environmental protection in Jilin Province The harm of high-energy consumption
Table3 Current situation of nitric oxide (Ton) emission for key industries in Jilin Province Industry Year 2006 2007 2008 2009 Thermal power industry 208542.6 226585.3 261908.3 288958.4 Electricity and heating power production and supply industry 288937.6 263760.67 283173.06 312779.12 Data source: first draft of “the 12th five-year” plans for environmental protection in Jilin Province Table4 Current situation of COD (Ton) emission for key industries in Jilin Province Year Industry 2005 2006 2007 2008 2009 Farm and sideline food processing industry 5480 9314 10628 11617 11278 Beverage manufacturing industry 9841 12069 9206 5354 6358 Data source: first draft of “the 12th five-year” plans for environmental protection in Jilin Province Reasons of high energy consumption and pollution of enterprises are as follows: 1) Laws and regulations on energy conservation and emission reduction of enterprises are imperfect, and legislation punishment is not enough
At present, as marketization degree of enterprise energy conservation and emission reduction improves, the contradiction between energy conservation and emission reduction and profit pursuit becomes increasingly prominent, especially when energy conservation and emission reduction can not make up the cost which has been paid, the motivation of enterprises to participate in energy conservation and emission reduction will be at a discount.
Meanings, Approaches and Strategies of Energy Conservation and Emissions Reduction.

The influences of reaction temperature and time on the reduction ratio of magnesia were studied.
The reduction ratio of magnesia increases with the increase in the reaction temperature and time.
The wMg is the weight fraction of magnesium in the briquette before thermal reduction.
Data were collected over the 2θ range of 10-90º.
Specially, the reduction ratio of magnesia can be up to 73% after 1 hrs aluminothermic reduction at 1200°C.

And then it substituted equation (8) with the data at 900-1050 ºC of Fig.2., we obtained Fig.3..
Therefore carbon gasification was controlling step of whole reduction process.
Fig.5 was obtained by substitution of the data of Fig.2 at 900-1050℃.
Fig.7 was obtained by substitution of the data of Fig.2 at 900-1050 ºC.
The reduction process of carbon-bearing pellet includes two stages and the reduction rate of the first stage is faster than the second one. 3.