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Partially missing or blurring attribute values make data become incomplete during collecting data.
First algorithm divided the data set into a complete data set and non complete data set, and then the complete data set was clustered using the affinity propagation clustering algorithm, incomplete data according to the design method of the similarity metric is divided into the corresponding cluster.
Introduction Clustering analysis is an important method in data mining field, when clustering incomplete data, due to the presence of missing data, the clustering result is obviously reduced.
The intuition of these strategies is discarding missing data, only clustering the complete data.
Zhang, "Novel algorithm for filling incomplete data of internet of things based on attribute reduction,” Computer Engineering and Design, vol. 34, no. 2, pp. 418-422, 2013

The grain size of the cold-rolled specimens decreased with increase of reduction rate, c.f., as the rolling reduction increased to 90%, grain size along the direction normal to the sheet decreased to about 8μm in thick.
At a reduction rate of 50%, the elastic deformation resulted in a further downward shift and an increase in diffraction peak width at the peak position than the reduction rate of 20%.
For the reduction rate of 90%, the peak width is further increased while the angle remains unchanged.
Obviously, the transition time decreases as the reduction rate and the annealing temperature increases.
Here, the JMAK model incorporates microhardness data, instead of relying on microstructure and use a unified equation.

Results show a reduction in system energy losses for the optimized values, without violating any power system requirements.
Two case studies will be conducted by utilizing the data from a typical 132kV main intake substation; and simulated using DIgSILENT® PowerFactory software [8].
Instead, the 24-hour energy loss (in kWh) of the feeder is to be minimized, which is calculated as below: Eloss= t = 024 - ∆tPloss(t) × ∆t (1) Ploss(t) is the active power loss of the network (in kW), returned by PowerFactory after the load flow for corresponding data at time t is executed.
∆t is the time period, taken as 0.05 hour (or 3 minutes), which is equal to the smallest available duration of data sampling.
The middle penetration appears to give the most loss reduction for this case, which is somewhat similar to the total base load of the feeder.

Many algorithms for fitting and noise-reduction of range data from single feature have been proposed.
Many researches have been taken on fitting and noise-reduction method of range data.
Fitting and Noise-reduction Algorithm for Single Standard Profile Feature Since what we got from metrological system is range data, fitting of single profile feature in the pattern is the premise.
Then new profile feature fitting process is done through the left range data, as well as the same noise reduction process.
We simulated the range data of 4 planes and 2 cylinders according to method of generating range data proposed in Geometrical product specifications (GPS) and verification (ISO 10360-6-2001), then we added different equally proposed random noise and transformed the range data to a specified location in space and used the range data as original data to test the validity and robustness of our algorithm.

Dosage in the 50 ~ 100mg/l to meet the water when the wellbore is less than 25% of the wax viscosity and single-tube process requirements gathering, can be compared to the multi-port wells, monitoring data before and after dosing showed that the crude oil after dosing freezing point decreased 5.6 ℃, the average rate of 87.4% viscosity reduction, the average rate of 91.5% paraffin.
Faced with this situation provides a well technician DRA(Drag reduction agent ) oil soluble formulation and preparation method.
Dosage in the 50 ~ 100mg/l to meet the water when the wellbore is less than 25% of the wax viscosity and single-tube process requirements gathering, can be compared to the multi-port wells, monitoring data before and after dosing showed that the crude oil after dosing freezing point decreased 5.6 ℃, the average rate of 87.4% viscosity reduction, the average rate of 91.5% paraffin.
Conclusion (1)After dosing oil solidifying point, viscosity, and a significant reduction in the amount of wax, can be compared by multi-port wells, monitoring data before and after dosing showed that the freezing point of crude oil after dosing decreased 5.6 ℃, the average rate of 87.4% viscosity reduction, The average rate of 91.5% paraffin.
(2) The oil-soluble DRA can be provided with the double functions of the drag reduction in single-tube gathering process and pipeline back pressure reduced

(3) According to the data in Fig.4, the relationship between the surface resistivity and Bi2O3 content can be described as followed equation (Eq. 4), and the linear correlation coefficient is 0.991, as shown in Fig. 5.
What’s more, the slope of experiment results is higher than data of Eq. 3, indicated that Bi2O3 has a more significant effect on reducing the surface resistivity of MCP glasses.
While an obvious diffraction peak appeared after reduction.
Fig. 7 XRD patterns of glass sample 2: (a) before reduction; (b) after reduction.
These can be confirmed by the spectral transmission curve after reduction.

Spectral data such as near-infrared spectrum and frequency spectrum can simply the modeling of the difficulty-to-measured parameters.
The latent variables space based feature extraction can successfully complete the dimension reduction and feature extraction of the spectral data.
Spectral data can simply the modeling.
The near-infrared spectral data come from http://www.ucl.ac.be, in which the data for learning and test are 150*700 and 68*700 respectively.
This approach can also be used to modeling high dimensional Radar and photometric data.

Then with statistical data, the change of CO2 emissions of industrial sectors can be attributed into the four factors just as the Eq.3: (3) where Δ is the difference between the current year and the reference which is the last year in this paper.
The energy consumption of industrial sectors is statistical data in China Energy Statistical Yearbook from 1990 to 2010.
The gross industrial output value (GIOV) of 39 industrial trades is statistical data in China Industry Economy Statistical Yearbook from 1990 to 2010 corrected as the latest values and calculated in Chinese Yuan at 2005 constant price.
The energy consumption of industrial trades is statistical data from 1990 to 2010 from China Energy Statistical Yearbook and corrected as the latest values.
The indirect CO2 emissions of per unit power and heat are calculated based on the data from energy balance sheet in China Energy Statistical Yearbook.

Unfortunately, in data management scenarios today it is rarely the case that all the data can be fit nicely into a conventional relational DBMS, or into any other single data model or system.
Most data management scenarios today rarely have a situation in which all the data can be not nicely into a conventional relational DBMS, or into any other single data model or system.
Unfortunately, in data management scenarios today it is rarely the case that all the data can be fit nicely into a conventional relational DBMS, or into any other single data model or system.
The data space is a collection of data[2] which is associated with the main body and its relationship to the body, all of the data in the data space can be controlled.
Therefore according to uncertain data, lots of data model and data management system are proposed such as TRIO, MayBMS and MystiQ and so on.

The theory accurately predicted polarization data and glucose hydrogenation rates. 1.
Circled points are experimental data and solid lines are calculated data using NLS fitting procedure.
It can be seen that the fitted curve for the total current, which is the sum of two partial currents, is in good agreement with the experimental data.
The experimental data are presented by circles.
The experimental data are presented by symbols.