mahout所實現的算法

https://cwiki.apache.org/confluence/display/MAHOUT/Algorithms 列出mahout所實現或正在實現的一些算法

Classification

Logistic Regression (SGD)

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)

Online Passive Aggressive (integrated, MAHOUT-702)

Boosting (awaiting patch commit, MAHOUT-716)

Hidden Markov Models (HMM) (MAHOUT-627, MAHOUT-396, MAHOUT-734) - Training is done in Map-Reduce

Clustering

Reference Reading

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 (MAHOUT-363 - integrated)

Minhash Clustering (MAHOUT-344 - integrated)

Top Down Clustering (MAHOUT-843 - integrated)

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)

Stochastic Singular Value Decomposition with PCA workflow (PCA and dimensionality reduction workflow is now integrated with SSVD)

Principal Components Analysis (PCA) (open)

Independent Component Analysis (open)

Gaussian Discriminative Analysis (GDA) (open)

Evolutionary Algorithms

• NOTE: * Watchmaker support has been removed as of 0.7

see also: MAHOUT-56 (integrated)

You will find here information, examples, use cases, etc. related to Evolutionary Algorithms.

Introductions and Tutorials:

• Evolutionary Algorithms Introduction

• How to distribute the fitness evaluation using Mahout.GA

Examples:

• Traveling Salesman

• Class Discovery

Recommenders / Collaborative Filtering

Mahout contains both simple non-distributed recommender implementations and distributed Hadoop-based recommenders.

• Non-distributed recommenders ("Taste") (integrated)

• Distributed Item-Based Collaborative Filtering (integrated)

• Collaborative Filtering using a parallel matrix factorization (integrated)

• First-timer FAQ

Vector Similarity

Mahout contains implementations that allow one to compare one or more vectors with another set of vectors.  This can be useful if one is, for instance, trying to calculate the pairwise similarity between all documents (or a subset of docs) in a corpus.

• RowSimilarityJob – Builds an inverted index and then computes distances between items that have co-occurrences.  This is a fully distributed calculation.

• VectorDistanceJob – Does a map side join between a set of "seed" vectors and all of the input vectors.

Other

• Collocations