Data Mining of Fractured Experimental Data Using Neurofuzzy Logic–Discovering and Integrating Knowledge Hidden in Multiple Formulation Databases for a Fluid-Ded Granulation Process

2008 ◽  
Vol 97 (6) ◽  
pp. 2091-2101 ◽  
Author(s):  
Q. Shao ◽  
R.C. Rowe ◽  
P. York
Keyword(s):  
Experimental Data ◽  
Data Mining ◽  
Granulation Process ◽  
Neurofuzzy Logic

Data Mining and Optimize TCM Prescription Compatibility Based on WDS-PLS

2014 ◽  
Vol 687-691 ◽  
pp. 1385-1388
Author(s):  
Zhuo Wang ◽  
Bin Nie ◽  
Ri Yue Yu
Keyword(s):  
Mathematical Modeling ◽  
Experimental Data ◽  
Data Mining ◽  
Regression Coefficient ◽  
Uniform Design ◽  
Herbal Medicines ◽  
Data Standardization ◽  
Modeling Data ◽  
Pls Method ◽  
Original Formula

Data mining and optimize traditional Chinese medicine (TCM) prescription compatibility based on wavelet denoise spectral and partial least squares (WDS-PLS). Method: First of all, experimental design: with reference to the original formula, the herbal medicines in a prescription designed nine formula based on mixing uniform design; Secondly, obtain experimental data and data standardization; Finally, mathematical modeling, data mining and optimize TCM prescription compatibility base on WDS-PLS.Results: gain the regression coefficient and equation, VIP sorting, loadings Bi plot, and seek out the optimized direction of the prescription. Conclusion: the method data mining and optimize the compatibility of the dachengqi decoction is feasible and effective.

The Novel Model of Construct Materials Science and Information Based on Association Rule Mining

2013 ◽  
Vol 327 ◽  
pp. 197-200
Author(s):  
Guo Fang Kuang ◽  
Ying Cun Cao
Keyword(s):  
Experimental Data ◽  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Materials Science ◽  
Frequent Itemsets ◽  
Data Sets ◽  
The Novel ◽  
Rule Mining ◽  
Novel Model

The material is used by humans to manufacture the machines, components, devices and other products of substances. Association rules originated in the field of data mining, people use it to find large amounts of data between itemsets of the association. Apriori is a breadth-first algorithm to obtain the support is greater than the minimum support of frequent itemsets by repeatedly scanning the database. This paper presents the construction of materials science and information model based on association rule mining. Experimental data sets prove that the proposed algorithm is effective and reasonable.

scholarly journals Application of Data Mining in the Guidance of Sports Training

2013 ◽  
Vol 765-767 ◽  
pp. 1518-1523
Author(s):  
Fan Hui Meng ◽  
Qing Li Li
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Experimental Data ◽  
Data Mining ◽  
Statistical Learning ◽  
Athletic Performance ◽  
Sports Training ◽  
Mining Technology ◽  
Broad Application ◽  
Use Of Data

Data mining is the techniques of finding the potential law from the data by machine learning and statistical learning .This paper focuses on a number of problems existed in the currents ports training, discusses the application principle of the data mining technology in sports training, and applies the critical neural networks for forecasting the performances of the athletes .Experimental data show that prediction of athletic performance by the use of neural network has very good approximation ability. It shows a broad application space of the use of data mining technology.

scholarly journals The Miracle of the Septuagint and the Promise of Data Mining in Economics

2009 ◽  
Author(s):  
Stan du Plessis
Keyword(s):  
Experimental Data ◽  
Data Mining ◽  
Economic Theory ◽  
Econometric Models ◽  
The Other ◽  
Empirical Science ◽  
Data Generating Process ◽  
Joint Constraints ◽  
Short Section

Data mining could compromise the believability of econometric models. And yet there might not be an alternative to data mining if economics is going to be an empirical science practiced with the joint constraints of incomplete economic theory and non-experimental data. The organizing principle for this discussion of data mining is a philosophical spectrum that sorts the various econometric traditions according to their epistemological assumptions about the underlying data-generating process (DGP), starting with instrumentalism at one end and reaching claims of encompassing the DGP at the other; call it the DGP-spectrum. In the course of exploring this spectrum, this article discusses various Bayesian, specific to general (S–G) as well as general to specific (G–S) methods. A description of data mining and its potential dangers and a short section on potential institutional safeguards to these problems set the stage for this exploration.

Improving knowledge of plant tissue culture and media formulation by neurofuzzy logic: A practical case of data mining using apricot databases

2011 ◽  
Vol 168 (15) ◽  
pp. 1858-1865 ◽  
Author(s):  
Jorge Gago ◽  
Olaya Pérez-Tornero ◽  
Mariana Landín ◽  
Lorenzo Burgos ◽  
Pedro P. Gallego
Keyword(s):  
Data Mining ◽  
Tissue Culture ◽  
Plant Tissue Culture ◽  
Plant Tissue ◽  
Practical Case ◽  
Media Formulation ◽  
Neurofuzzy Logic

scholarly journals Subsymbolic methods for data mining in hydraulic engineering

2000 ◽  
Vol 2 (1) ◽  
pp. 3-13 ◽  
Author(s):  
Anthony W. Minns
Keyword(s):  
Experimental Data ◽  
Data Mining ◽  
A Priori ◽  
Empirical Relation ◽  
Exact Form ◽  
Symbolic Form ◽  
Process Characteristics ◽  
Evolutionary Force ◽  
The One ◽  
Insight Into

This paper describes the results of experiments with artificial neural networks (ANNs) and genetic programming (GP) applied to some problems of data mining. It is shown how these subsymbolic methods can discover usable relations in measured and experimental data with little or no a priori knowledge of the governing physical process characteristics. On the one hand, the ANN does not explicitly identify a form of model but this form is implicit in the ANN, being encoded within the distribution of weights. However, in cases where the exact form of the empirical relation is not considered as important as the ability of the formula to map the experimental data accurately, the ANN provides a very efficient approach. Furthermore, it is demonstrated how numerical schemes, and thus partial differential equations, may be derived directly from data by interpreting the weight distribution within a trained ANN. On the other hand, GP evolutionary force is directed towards the creation of models that take a symbolic form. The resulting symbolic expressions are generally less accurate than the ANN in mapping the experimental data, however, these expressions may sometimes be more easily examined to provide insight into the processes that created the data. An example is used to demonstrate how GP can generate a wide variety of formulae, of which some may provide genuine insight while others may be quite useless.

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