Introduction.- Fundamental Concepts.- Data Architecture and Data Modeling.- Representing Data Mining Results.- The Input Side of the Equation.- Statistical Methods.- Bayesian Statistics.- Machine Learning Techniques.- Classification and Prediction.- Informatics.- Systems Biology.- Let's Call it a Day.- Appendix A.- Appendix B.- Appendix C. Appendix D.- Index.
Data mining provides a set of new techniques to integrate, synthesize, and analyze tdata, uncovering the hidden patterns that exist within. Traditionally, techniques such as kernel learning methods, pattern recognition, and data mining, have been the domain of researchers in areas such as artificial intelligence, but leveraging these tools, techniques, and concepts against your data asset to identify problems early, understand interactions that exist and highlight previously unrealized relationships through the combination of these different disciplines can provide significant value for the investigator and her organization.
Data mining involves uncovering the patterns inherent within the data
Provides a set of techniques that can help the life scientist leverage the valuable data asset
With each set of techniques are tangible examples to support them