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But calculations of scatter matrixes depend on labeled data, so this criterion can not be used in unsupervised pattern.
But scatter matrix based class separability criterion is used in supervised pattern because scatter matrix can not be calculated by unlabelled data.
If the difference between and its last value less than or the number of iterations no less than the given value, choose the eigenvectors corresponding to the top largest eigenvalues of the matrix as transformation matrix ，and use the linear transformation to finish dimensionality reduction, then terminate, otherwise go to Step 2.
Table 2 Rand Index values of K-means clustering results for original datasets and reduced-dimensional datasets extracted by PCA and FSM Datasets No Dimensionality reduction PCA FSM Iris 0.950 0.950 0.957 Wine 0.688 0.719 0.753 Wdbc 0.750 0.750 0.863 Glass 0.706 0.707 0.713 Table 3 Rand Index values of FCM clustering results for original datasets and reduced-dimensional datasets extracted by PCA and FSM Datasets No Dimensionality reduction PCA FSM Iris 0.950 0.942 0.957 Wine 0.688 0.711 0.770 Wdbc 0.750 0.750 0.863 Glass 0.727 0.707 0.707 Conclusion and Future Work This paper presents a novel class separability criterion based unsupervised feature extraction algorithm.

In the reaction tests, the OCs were submitted to successive reduction and oxidation cycles.
All these data were determined based on different times employed in the reduction stage, considering that the values corresponding ​​to the ratios between H2 and CO concentrations were calculated through GC and MS methods. 3.
Bulk nickel oxide showed a reduction between 260 to 560°C with a maximum at 400°C.
Temperature-programmed reduction profiles for OCs and bulk NiO.
It was also observed, during the redox cycles performance with smaller reduction times (3, 1.5 and 0.75 min, not shown in this work) that the time reduction at the reduction step reduces the carbon deposition by methane catalytic decomposition over Ni0 catalytic sites during the reduction of OC (CH4 ↔ C(s) + 2H2).

Maximum temperatures are around Tmax= 2540 K, and velocity vectors, indicated by arrows, exhibit 1.4 m/s maximum values , (c) experimental melt-pool (400 W – 0.4 m/min – Dm=2 g/min) Influence of process parameters on surface finish - Topography data were analyzed to identify their sensibility versus process parameters (P0 (W), V (m/min) and Dm (g/min)).
In Fig.4b, a strong reduction of Wt parameter with scanning speed is evidenced, but a rather limited effect of laser power P0.
An increase in V values provokes simultaneously a quasi-linear reduction of Dh values (eq.1 [3]), a decrease of MP length L and height H.
For a similar mass feed rate (3 g/min), experimental data (Fig.5a) indicate a moderate reduction (–25%) of peak values Rt together with an increase of V and P0, and constant average roughness values Ra.
Oncoming investigations will consider the following data as possible contributors to surface roughness and waviness: (1) the dilution ratio H/Dh, and the aspect ratios of the melt-pool : L/e and H/e

The algorithm processes the properties of the data objects, which determines the density of data object by counting the number of similar data objects and selects the center of categories according to the density of data object.
In this paper, inspired from the ideas of a fast K-Means clustering algorithm based on grid data reduction [9], we propose an enhanced K-Means algorithm based on category initial point selection (BIPSD_K).
Then, statistics the number of data objects which fall in the same interval, calculating the density of the data object.
Secondly, the data set is divided data set by iterative loop.
S= {S1, S2,…,Sn} is n dimensional data space, and Si= [Li, Hi].

In some cases, empirical data and the experience based design procedure can be combined with Finite Element Method (FEM) based analysis to reduce time and cost.
The methodology for process sequence design combines experience based empirical data obtained through previous designs, design rules and numerical simulations including plastic deformation and friction.
The design predictions, made by using empirical data and FEM, were compared with experimental data.
For the given reduction and initial blank diameter (49.53 mm), the punch diameter is 28.98 mm.
The experimental data was obtained for the strains up to 0.53.

In both cases a calibration procedure based on hardness data was involved.
Input data for these models were established by involving a calibration procedure based on hardness data.
If some parameters as Qd and Qs energy terms can be found or calculated from literature data, the others parameters of JMAK model and Myhr and Grong model need to be calibrated against experimental data.
The hardness data were then converted to X using Eq. 1 with HVmin = 78 and HVmax = 155.
One can notice that some of the data were not well fitted by the master curve.

In this study, the reduction technique of stiffness matrix, which is refered to “transfer matrix method (TM)“, is developed to solve effectively the problem.
In this study, the reduction technique of stiffness matrix, which is refered to “transfer matrix method (TM)“, is developed to solve effectively the structural problem composed of curved and straight pipes.
Applying Eq. (2) to pipe number 1 firstly, we have, 1Zb=1F1Za (3) Fig. 1 Pipe system In order to transfer Eq. (1) to state vector in the relation for local coordinate fixed in pipe number 2, the rotation matrix Rθ1,2 is multiplied from left side to Eq. (1), which leads to, 2Za=Rθ1,21Zb→Rθ1,21F1Za (4) Repetition of this operation in sequence gives us the following: 2Za=Rθ1,21F1Za 3Za=Rθ2,32FRθ1,21F1Za 4Za=Rθ3,43FRθ2,32FRθ1,21F1Za ⋯⋯⋯⋯ Consequently, the transfer relation from pipe number 1 to N can be expressed by, NZa=i=2NRθi-1,ii-1F∙1Za (5) which allows us to calculate the state vector NZa for pipe number N as output data from the state vector 1Za for pipe number 1 as input data.
Fig. 6 Stress distributions in case of Fig. 7 Relationship between accuracy 40 mm inner diameter and pipe bend factor Fig. 8 Comparison in stress components between TM and FEM Conclusion In this study, the reduction technique of stiffness matrix, which is refered to “transfer matrix method (TM)“, is developed to solve effectively the structural problem composed of curved and straight pipes.

Data analysis shows a significant decrease in all pollutant parameters after 24 hours of retention time.
The following are the formulas used for data analysis; Pollutants Removal Efficiency.
Based on the data above, it can be concluded that the initial wastewater quality from Samudera Raya restaurant does not meet the standard.
The COD reduction of 95.01% is obtained after 12 days of retention time.
Efficiency based on wastewater pollutant level reduction Time (hr) BOD TSS Oil & Grease COD Efficiency Efficiency Efficiency Efficiency (%) (%) (%) (%) 24 98,69 97,58 99,5 94,6 48 99,01 99,25 99,82 96,72 72 99,8 99,25 99,64 95,87 Data analysis shows a significant decrease in all pollutant parameters after 24 hours of retention time.

The Sulfur Dioxide atmospheric environmental capacity of Chengdu urban agglomeration was calculated by using this model so as to provide basic data for the total capacity control of Chengdu urban agglomeration.
Therefore, in the present study, we have collected the meteorological data of the recent three years measured by all the meteorological stations in the urban agglomeration.
The calculation method of yield reduction rate of crops is described bellow.
According to the acid rain monitoring data [20] in the recent 5 years of five cities of Chengdu urban agglomeration, in the rain, [SO4]2-∕[NO3]- =5.4, which indicates that the sulfuric acid type acid rain is dominant in this area.
Therefore, in economically developed areas, the multi source mode – double objective optimized method can be used to calculate the atmospheric environmental capacity, while in economically developing areas, the multi source mode - linear optimized method can be used to calculate the atmospheric environmental capacity which will be used as the basic data for total capacity control.

In addition, ratings indicators are much more indicators, which in turn will increase the difficulty in the work of data collection and arrangement.
Sample selection: Choose 38 Hotels in Beijing, Guangzhou, Nanjing, Ningbo and so on as the object of research, 8 hotels of which as verification set data, 30 as test set data (W1, W2, ...
W30) Data Collection: 150 survey questionnaires were distributed, and 139 available questionnaires were retrieved which resulted in 97.3% sample return and 92.6% the ratio of callback of valid questionnaire.
After careful interviewing, ultimately determine W20 as the Autocracy type of TMT, and W10 and W29 as democratic type of TMT. 3.2 Data processing and analysis 3.2.1 Test of Reliability and validity of Indexes’ system BY use of Cronbach α-reliability coefficient assessing the credibility, after the original data are standardized, then put it in the formula testing reliability and validity, calculated result as shown in Table 2.
Weight coefficient matrix of indicators multiply the corresponding value of the research data standardized, the value obtained is based on the one based on Rough Set.