Semi-hierarchical correspondence cluster analysis and regional geochemical pattern recognition

2007 ◽  
Vol 93 (2) ◽  
pp. 109-119 ◽  
Author(s):  
Hongjin Ji ◽  
Daoming Zeng ◽  
Yanxiang Shi ◽  
Yangang Wu ◽  
Xisheng Wu
Keyword(s):  
Cluster Analysis ◽  
Pattern Recognition ◽  
Geochemical Pattern

Geochemical pattern recognition through matrix decomposition

2019 ◽  
Vol 104 ◽  
pp. 670-685 ◽  
Author(s):  
Hamid Zekri ◽  
Ahmad Reza Mokhtari ◽  
David R. Cohen
Keyword(s):  
Pattern Recognition ◽  
Matrix Decomposition ◽  
Geochemical Pattern

Cluster analysis and multiplet pattern recognition in two-dimensional NMR spectra

1990 ◽  
Vol 89 (3) ◽  
pp. 543-552
Author(s):  
Klaus-Peter Neidig ◽  
Rainer Saffrich ◽  
Michael Lorenz ◽  
Hans Robert Kalbitzer
Keyword(s):  
Cluster Analysis ◽  
Pattern Recognition ◽  
Nmr Spectra ◽  
Two Dimensional

scholarly journals Use of Pattern Recognition Analysis to Identify Underlying Relationships of Doxorubicin Derivatives Optimized for Breast Cancer Treatment

ISRN Oncology ◽  
2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
Ronald Bartzatt
Keyword(s):  
Breast Cancer ◽  
Cluster Analysis ◽  
Pattern Recognition ◽  
Hierarchical Cluster Analysis ◽  
In Silico ◽  
Water Solubility ◽  
Hierarchical Cluster ◽  
Neighbor Joining ◽  
Analysis Of Similarity ◽  
Pattern Recognition Analysis

Introduction. Treatment of breast cancer includes surgery, drugs (hormone therapy and chemotherapy), and radiation. A discussion of eight drug constructs for the treatment of breast cancer, derived through application of in silico optimized molecular properties and substituent substitution, are analyzed using pattern recognition techniques. Methods and Materials. Determined properties of these eight compounds (inclusive of doxorubicin) showed a Log P varying from 0.567 to 4.137, rotatable bonds from 5 to 12, polar surface area from 195.1 A2 to 206.1 A2, and water solubility from 0.00873 mg/L to 390 mg/L. Analysis of similarity (ANOSIM), hierarchical cluster analysis, and neighbor-joining cluster analysis elucidated relationships among the drugs that are useful for pharmaceutical consideration. Results and Discussion. Although the new derivatives share the same parent scaffold (doxorubicin), elucidation by analysis of similarity (ANOSIM) indicates that these assorted compounds are substantially distinct. The number of oxygen and nitrogen atoms (hydrogen bond acceptors) remained constant at 12 for compounds. Although violations of the Rule of five remained constant at three for all compounds, the variation of Log P and water solubility offers potentially beneficial medicinal activity for this group of anticancer agents that may enhance the antitumor activity of these anthracycline antibiotics. Hierarchical cluster analysis results clearly differentiated the parent doxorubicin from all higher molecular weight analogs. This outcome is confirmed with the use of neighbor-joining cluster analysis. Conclusion. By utilizing in silico optimization with pattern recognition analysis, potentially advantageous analogs can be elucidated from known effective pharmaceuticals.

Research on Influence Factors of Strata Classification Based on Fuzzy Cluster Analysis and Fuzzy Pattern Recognition

2012 ◽  
Vol 594-597 ◽  
pp. 2843-2846
Author(s):  
Shuang Yue Liu ◽  
Li Na Chen
Keyword(s):  
Cluster Analysis ◽  
Pattern Recognition ◽  
Influence Factors ◽  
Fuzzy Model ◽  
Site Investigation ◽  
Original Data ◽  
Fuzzy Cluster ◽  
Fuzzy Cluster Analysis ◽  
Fuzzy Pattern Recognition ◽  
Fuzzy Pattern

Traditionally, it is usually depended on engineers’ personal experience and habits to establish the strata layers on the basis of the original data when they doing the desk studies of a site investigation work, which are greatly subjective and nondeterministic. A method in combining the fuzzy cluster analysis and fuzzy pattern recognition is presented to classify stratum. Firstly, the sample integration is classified by adopting clustering analysis method. The fuzzy model is set up for different degree. Afterwards, the undetermined forecasting sample is predicted by applying fuzzy pattern recognition. Through actual validation, the reliability of the prediction is tested and verified.

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