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“Advances in knowledge discovery and data mining”[M],AAAI/MIT press, 1996. pp. 83-115
“Data mining: An overview form database perspective[J], IEEE Trans.
Knowledge and Data Engineering,1996,8, pp. 833-866
Data-drive discovery of quantitative rules in relational databases[J] IEEE Trans.
Knowledge and Data Engineering,1993,5, pp. 29-40 [8] R.GOLAN.

For this paper, we use the Co loading values measured by EDS analysis in the data analysis and discussion.
Fig. 3 Koutecky-Levich plots for the ORR on CoNC coated glassy carbon electrodes Fig. 3 shows the Koutecky-Levich plots for the ORR data taken at the potential from -0.1 to 0.2 V.
The data comes from the current-potential curves (Fig. 2) obtained using CoNC2.
Its approach contrasts with the theoretical plots for 2- and 4-electron reduction reaction in Fig. 3.
It could be suggested that the CoNC2 can alter the ORR reaction mechanism via both the 2-electron reduction reaction and the 4-electron reduction reaction. 2.3 Scanning electron microscopy measurement Fig. 4 shows SEM images of a sample of CoNC2 catalyst magnified 5000 and 10000 times.

It can be effective for large-scale incomplete ocean data reduction and it also provides a strong basis for decision making for the marine environment processing and follow-up processing.
The prevalence of incomplete data in marine monitoring and other areas of the internet of things bring tremendous difficulties to data fusion, data mining.
In order to mining knowledge from incomplete data, Literature [6] constructed a new similar relationship.
These studies are for static data, but in reality in many databases are dynamic.
Conclusions The traditional approach to deal with incomplete data is make it completed.

Fig. 1 X-ray diffraction data for the mixture of Fe2O3-Ca MA powders as a function of total milling time.
Fig. 4 X-ray diffraction data for the mixture of Fe2O3-Ca MA powders ball-milled for various time intervals and subsequently heat treated up to 600°C.
Fig. 4 shows the X-ray diffraction data for the mixture of Fe2O3-Ca MA powders ball-milled for various time intervals and subsequently heat treated up to 600°C.
Hence, the magnetic data of Fe2O3-Ca MA powders can supply a better understanding of the evidences for the solid state reduction process of Fe2O3-Ca system, as well as the amount of magnetic phase and microstructures.
The X-ray diffraction and magnetic data have been discussed simultaneously in order to achieve a better understanding of the solid state reduction induced by MA.

Our data structure is a practical linear space data structure that supports range selection queries in time with preprocessing time.
Our data structure is a complete binary tree.
This reduction is applied recursively by the algorithm.
This reduction is applied recursively by the algorithm.
The new data structure has similar or better speed than existing data structures but uses less space in the worst case.

The kinetic parameters of each stage of LiFePO4 material prepared by carbothermal reduction method were calculated using the Doyle-Ozawa and Kissinger methods.
Carbonthermal reduction technology has been studied by lots of domestic and foreign researchers, however kinetics of carbonthermal reduction process is rarely investigated.
LiFePO4 material is synthesized by carbothermal reduction method using FePO4 as iron sources in this paper.
The optimized synthesis process and the design and optimization of equipment for mass production could be obtained by the data and results. 2.
The apparent activation energy of each step of LiFePO4 material prepared by carbothermal reduction method is calculated using the Doyle-Ozawa and Kissinger methods.

The experimental data were collected to obtain training set and testing set.
The results of the ANN model were in good agreement with experimental data.
Finally, 59 groups of data were obtained by experimented.
But, the neural network can be easily over-fitting, which causes the error rate on new unseen data to be much larger than the error rate on the training data.
Fig. 2 shows the root mean square error for both testing data and training data for various numbers of units in the hidden layer by means of trainbr algorithm.

Degradation of Azo Dyes by the Reduction and Oxidation with Nano Sized Zero Valented Iron B.H.
Pre-reduction by nZVI could convert azo dye to products whose oxidation is more degradable and therefore enhances the removal efficiency.
The successful use of ZVI has been reported to decolorize azo dye solution by reduction of the -N=N- bond at the iron surface [2,3,4,5].
The laboratory synthesized nZVI used in this study had the specific surface area of 27.9m2/g, which is smaller than the data reported in the literature.
Compared to various H2O2 dosages, pre-reduction of Orange II with nZVI enhanced the TOC removal by 5~15%.

This paper describes a dimension reduction method of input vector to improve classification efficiency of LVQ neural network, where GA is used to decrease the redundancy of input data.
Dimension reduction by GA is present in section Ⅲ.
denotes the number of samples in test data set.
· The Origin of Data In this paper, we choose the UCI data sets [7] as our test data, which are considered the standard data sets to compare the capability of various algorithms in data mining domain.
TABLE Ⅰ Experimental Data Sets Data Set Number of Attributes Number of Instances Number of Classes Ionosphere 34 351 2 Vehicle 18 946 4 Sonar 60 208 2 Waveform 40 5000 3 Breast Cancer 32 569 2 Vote 16 436 2 · Evaluative Method For each data set we chose in the table 1, 80 percent data instances are selected at random as training data, and the 20 percent remainder data instances are considered as test data.

The key technology of the liquid core heavy reduction rolling mill relates to the roller’s normal life cycle in a bad work condition.
Introduction The roller is the important component of the liquid core heavy reduction rolling mill.
Use tensile performance data to estimate the fatigue constant.
This paper use four-point method to calculate the fatigue constant, data can be obtained by monotonic tensile
Shen: Mechanical Engineering Material Properties Data Manual (China Machine Press, China 1994)