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Mining of biological data II: assessing data structure and class homogeneity by cluster analysis
R T Kamimura1, S Bicciato, H Shimizu
1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02319, USA.
Metabolic Engineering
|November 1, 2000
Summary
This study introduces a novel clustering algorithm for data preprocessing. It improves model quality by identifying similar samples, enhancing training set selection and atypical sample detection in multivariate data analysis.
Area of Science:
- Data Science
- Bioinformatics
- Machine Learning
Background:
- Class assignment in data analysis often relies on broad characteristics, potentially masking underlying sample dissimilarities.
- This can lead to reduced model quality and predictive power in empirical modeling.
- Accurate sample grouping is crucial for robust data analysis.
Purpose of the Study:
- To present a new clustering algorithm for data preprocessing.
- To improve the selection of training sets by identifying homogeneous multivariate data.
- To detect atypical samples that may be misclassified.
Main Methods:
- The algorithm combines cluster analysis and principal component analysis (PCA).
- It applies agglomerative clustering to the first principal component of the data matrix.
- Multivariate data homogeneity is analyzed to group similar samples.
Main Results:
- The method effectively identifies and groups samples with similar data structures into distinct clusters.
- It facilitates the formation of more appropriate training sets for empirical models.
- Atypical lots, exhibiting properties of one class but assigned to another, can be identified.
Conclusions:
- The proposed clustering algorithm enhances data preprocessing for empirical model development.
- It improves the identification of fundamentally similar samples, leading to better training sets.
- The technique offers a valuable tool for analyzing multivariate data homogeneity and detecting misclassified samples.