Data mining means to retrieve useful information from data with respect to a data model. Machine learning seeks to identify behavior patterns in data, and them build various models based on observed patterns.

Data mining has its strong focus on working with industrial problems and getting practical solutions. Therefore it concerns with not only data size (large data), but also data processing speed (stream data). Below shows a general overview of some data mining tasks, algorithms and examples.

Data Mining uses Machine Learning algorithms to mine big datasets.

One practical application to Data Mining is in the area of Bioinformatics and Life Sciences which have large-scale genetic/genomic data sets. A worth mentioning useful resource for the analysis of such genetic/genomic data with R is the Bioconductor. One such mining of genomic databases with R can be seen here.

Useful Resources

1) Rattle: A Graphical User Interface for Data Mining using R

2) Data Mining with Rattle and R

3) Rattle package for R

4) Introduction to Data Mining with R

5) Data Mining Applications with R

6) R and Data Mining: Examples and Case Studies

7) R Reference Card for Data Mining

8) Machine Learning with R

9) Machine Learning Cheat Sheet

10) Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner

11) Data Mininig and Knowledge Discovery Handbook (Second Edition)

12) A Quick Guide to Large Scale Genomic Data Mining

13) Essential of Machine Learning Algorithms

14) Machine Learning Modelling in R

This link provides us on How to Get Started with Machine Learning Algorithms in R.

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