Potential methods in pattern recognition

Coomans, Danny

doi:10.1016/s0003-2670(01)83197-4

Cited by 27 publications

(2 citation statements)

References 8 publications

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“…It can be observed that performances of the two methods are practically coincident. In the kNN rule, the increment in the correct classification obtained when one passes from k = 1 to k = 5 is possibly due to the larger sample size of Prosecco wine with respect to Soave and Verduzzo [see Coomans and Massart (1982a)].…”

Section: Resultsmentioning

confidence: 99%

Multiple discriminant analysis in the analytical differentiation of Venetian white wines. 4. Application to several vintage years and comparison with the k nearest-neighbor classification

Moret¹,

Leo²,

Giromini³

et al. 1984

J. Agric. Food Chem.

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Section: Resultsmentioning

confidence: 99%

Multiple discriminant analysis in the analytical differentiation of Venetian white wines. 4. Application to several vintage years and comparison with the k nearest-neighbor classification

Moret¹,

Leo²,

Giromini³

et al. 1984

J. Agric. Food Chem.

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“…Starting with a distance matrix, several techniques can be applied to identify clusters [2][3][4][5][6]. One of the more popular ones is Ward's hierarchical clustering [2 and 7].…”

Section: Urmentioning

confidence: 99%

Patterns in clinical chemistry requests

Hemel

Hindriks

Voet

et al. 1989

Journal of Analytical Methods in Chemistry

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For each patient sample that is presented to the clinical chemistry laboratory a combination of various tests can be requested. This combination or profile will depend on the condition of the patient, and hence also on the requesting hospital department. Several techniques were applied to detect and describe patterns in tests requested by the cardiology, hepatology and nephrology sections of the out-patient's Department for Internal Medicine. Comparison of the frequencies of ordering the tests showed significant differences between these sections. Cluster analysis and multidimensional scaling were used to show similarities and differences in the test profiles that were used by the sections. These techniques are useful for generating hypotheses, but the statistical significance of the clustering found is difficult to assess.

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SONHICA (Simple optimized non-HIerarchical Cluster Analysis): A new tool for analysis of molecular conformations

et al. 1997

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We describe a new clustering program, SONHICA (Simple Optimized Non‐HIerarchical Cluster Analysis), developed to analyze large data sets of molecular conformations. Unlike traditional clustering methods, SONHICA does not make use of an overall index, like a distance, to evaluate similarity between objects. Each descriptor variable is compared individually on the basis of a preset threshold value. This assures high control and sensitivity over the input variables. In addition, periodic and nonperiodic descriptors, such as dihedral angles and interatomic distances, can easily be used together. SONHICA generates clusters with the highest possible density and all pairs of objects within a cluster are similar. These features make SONHICA particularly suitable for the analysis of data sets which tend to form globular clusters. This method was applied to the analysis of a modified linear tetrapeptide, ITF1697, under investigation for its anti‐ischemic properties, and a cyclic pentapeptide, BQ123, a potent antagonist of endothelin A. On the basis of the results presented here, SONHICA appears to be an interesting new tool in the field of the clustering methods applied to the analysis of molecular conformations. © 1997 John Wiley & Sons, Inc. J Comput Chem 18: 1295–1311, 1997

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Potential methods in pattern recognition

Cited by 27 publications

References 8 publications

Multiple discriminant analysis in the analytical differentiation of Venetian white wines. 4. Application to several vintage years and comparison with the k nearest-neighbor classification

Multiple discriminant analysis in the analytical differentiation of Venetian white wines. 4. Application to several vintage years and comparison with the k nearest-neighbor classification

Patterns in clinical chemistry requests

SONHICA (Simple optimized non-HIerarchical Cluster Analysis): A new tool for analysis of molecular conformations

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