Pattern recognition approaches fall into two main categories: supervised and unsupervised. Supervised systems require knowledge or data in which the outcome or classification is known ahead of time, so that the system can be trained to recognize and distinguish outcomes. Unsupervised systems cluster or group records without previous knowledge of outcome or classification. The most frequently used unsupervised pattern recognition approach is principal component analysis (PCA). Other unsupervised methods include hierarchical clustering, k-means, and self organizing maps (SOM).

The Pattern Recognition Tool provides principal component analysis (PCA), linear discriminate analysis (LDA), and canonical discriminate analysis (CDA) for data clustering. The six graphics generated by the Pattern Recognition tool are available in the output visualization window.

Input for the Pattern Recognition tool includes:

• the normalized data file generated from the Normalization Tool for a collection of LC-MS datasets aligned through the Peak Alignment Tool.
• input parameters to provide information about the number of groups and group labels for the normalized dataset.

Researchers should cite this work as follows:

• Ann Christine Catlin; George Howlett (2011), "Pattern Recognition for Normalized LC-MS Data," http://ccehub.org/resources/pattern.

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1. proteome discovery pipeline
2. proteomics
3. statistical models