Handbook of Statistics, Vol. 2. Classification, Pattern Recognition and Reduction of Dimensionality.

It is crucial to measure dust concentration precisely, but it normally varies with changes of working conditions. To increase precision and on-line performance of coal dust sensor, an adaptive pattern recognition algorithm was presented. The signals of unitary angular spectrums were chosen as the adaptive eigenvector for pattern recognition and pattern bank was established in advance. Furthermore, the ratio of the sum of inner signals to that of outer signals about the diffraction angular was considered as the eigenvector of subclass pattern classification. After classification, pattern could be recognized easily and rapidly. Subsequently, number of detailed patterns within different pattern groups was increased reasonably. The errors of total coal dust and respiring coal dust decline from 6% to 2.5% and from 9% to 3%, respectively. As a result, the precision of sensor achieves 95% during the measurement. It can be concluded that the adaptive pattern recognition algorithm is effective to improve the precision and real-time performance of coal dust sensor.

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Automated Diagnosis of Craniofacial Pain

Journal of Dental Research ◽

10.1177/00220345730520062401 ◽

1973 ◽

Vol 52 (6) ◽

pp. 1297-1302 ◽

Cited By ~ 5

Author(s):

Michael S. Leonard ◽

Stephen D. Roberts ◽

Thomas B Fast ◽

Parker E. Mahan

Keyword(s):

Pattern Recognition ◽

Diagnostic Classification ◽

Automated Diagnosis ◽

Classification Pattern ◽

Craniofacial Pain ◽

Automated Process ◽

Automated Classifier ◽

Classifier Training

One of the techniques of automated diagnostic classification, pattern recognition, was applied to the problem of classifying patients with craniofacial pain. Dental data relevant to the classification process and the automated classifier are presented. Classifier training and accuracy of the automated process are discussed.

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Reduction of Dimensionality, Order, and Classification in Spaces of Theoretical Descriptions of Molecules: An Approach Based on Metrics, Pattern Recognition Techniques, and Graph Theoretic Considerations

Statistical Modelling of Molecular Descriptors in QSAR/QSPR ◽

10.1002/9783527645121.ch13 ◽

2012 ◽

pp. 349-364

Author(s):

George Maroulis

Keyword(s):

Pattern Recognition ◽

Pattern Recognition Techniques ◽

Graph Theoretic ◽

Reduction Of Dimensionality

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The Impact of Missing Data on Data Mining

Data Mining ◽

10.4018/978-1-59140-051-6.ch007 ◽

2011 ◽

pp. 174-198 ◽

Cited By ~ 4

Author(s):

Marvin L. Brown ◽

John F. Kros

Keyword(s):

Data Mining ◽

Pattern Recognition ◽

Missing Data ◽

Association Rules ◽

Actual Data ◽

Inaccurate Data ◽

Sample Data ◽

Classification Pattern ◽

Model Training ◽

The Impact

Data mining is based upon searching the concatenation of multiple databases that usually contain some amount of missing data along with a variable percentage of inaccurate data, pollution, outliers, and noise. The actual data-mining process deals significantly with prediction, estimation, classification, pattern recognition, and the development of association rules. Therefore, the significance of the analysis depends heavily on the accuracy of the database and on the chosen sample data to be used for model training and testing. The issue of missing data must be addressed since ignoring this problem can introduce bias into the models being evaluated and lead to inaccurate data mining conclusions.

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