Apache Mahout: a formidable collection of Data mining algos on the top of Hadoop (Map Reduce)

I am playing with Mahout these days and I am loving it. Mahout is a formidable collection of data mining and machine learning algorithms implemented on the top of Hadoop, the open source Map Reduce programming paradigm.

Here you find some hints to run Mahout on the top of Amazon EC2. Here a collection of algorithms implemented . They include:

Classification

Logistic Regression (SGD implementation in progress: MAHOUT-228)

Bayesian

Support Vector Machines (SVM) (open: MAHOUT-14, MAHOUT-232 and MAHOUT-334)

Perceptron and Winnow (open: MAHOUT-85)

Neural Network (open, but MAHOUT-228 might help)

Random Forests (integrated - MAHOUT-122, MAHOUT-140, MAHOUT-145)

Restricted Boltzmann Machines (open, MAHOUT-375, GSOC2010)

Clustering

Canopy Clustering (MAHOUT-3 - integrated)

K-Means Clustering (MAHOUT-5 - integrated)

Fuzzy K-Means (MAHOUT-74 - integrated)

Expectation Maximization (EM) (MAHOUT-28)

Mean Shift Clustering (MAHOUT-15 - integrated)

Hierarchical Clustering (MAHOUT-19)

Dirichlet Process Clustering (MAHOUT-30 - integrated)

Latent Dirichlet Allocation (MAHOUT-123 - integrated)

Spectral Clustering (open, MAHOUT-363, GSoC 2010)

Pattern Mining

Parallel FP Growth Algorithm (Also known as Frequent Itemset mining)

Regression

Locally Weighted Linear Regression (open)

Dimension reduction

Singular Value Decomposition and other Dimension Reduction Techniques (available since 0.3)

Principal Components Analysis (PCA) (open)

Independent Component Analysis (open)

Gaussian Discriminative Analysis (GDA) (open)

Evolutionary Algorithms

see also: MAHOUT-56 (integrated)