The top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006, which are presented in [this](http://link.springer.com/article/10.1007/s10115-007-0114-2) article and [this](http://www.crcpress.com/product/isbn/9781420089646) subsequent book are the following: 

- [C4.5](http://en.wikipedia.org/wiki/C4.5_algorithm), 
- [k-Means](http://en.wikipedia.org/wiki/K-means_clustering), 
- [SVM](http://en.wikipedia.org/wiki/Support_vector_machine) or support vector machines; 
- [Apriori](http://en.wikipedia.org/wiki/Apriori_algorithm), 
- [EM](http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm) or Expectation-Maximization, 
- [PageRank](http://en.wikipedia.org/wiki/PageRank), 
- [AdaBoost](http://en.wikipedia.org/wiki/AdaBoost) or adaptive boosting, 
- [kNN](http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) or k nearest neighbors, 
- [Naive Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier), 
- [CART](http://en.wikipedia.org/wiki/Decision_tree_learning) or classification an regression trees. 
 
See [here](http://www.quora.com/What-are-the-top-10-data-mining-or-machine-learning-algorithms) for further discussions and viewpoints on the importance of more recent algorithms in data mining.