一、协同过滤 Collaborative Filtering
　　1、基于用户的协同过滤 User-Based Collaborative Filtering
　　2、基于项目的协同过滤统 Item-Based Collaborative Filtering
　　3、交替最小二乘张量分解 Matrix Factorization with Alternating Least Squares
　　4、基于隐式反馈的交替最小二乘张量分解 Matrix Factorization with Alternating Least Squares on Implicit Feedback
　　5、加权矩阵分解 Weighted Matrix Factorization, SVD++, Parallel SGD
　　基于用户（User-based）的协同过滤和基于项目（Item-based）的协同过滤统称为以记忆为基础（Memory based）的协同过滤技术，他们共有的缺点是资料稀疏，难以处理大资料量影响即时结果，因此发展出以模型为基础的协同过滤技术。

二、分类
　　1、逻辑回归 Logistic Regression - trained via SGD
　　2、朴素贝叶斯算法 Naive Bayes/ 互补贝叶斯分类算法 Complementary Naive Bayes - MapReduce
　　3、随机森林 Random Forest - MapReduce
　　4、隐马尔可夫模型 Hidden Markov Models - single machine
　　5、多层感知机 Multilayer Perceptron - single machine

三、聚类 Clustering
　　1、Canopy聚类算法 Canopy Clustering - single machine / MapReduce (已经遗弃,当K-means算法足够成熟的时候将会被删除)
　　2、k-Means聚类 k-Means Clustering - single machine / MapReduce
　　3、模糊k-Means聚类 Fuzzy k-Means - single machine / MapReduce
　　4、Streaming k-Means - single machine / MapReduce
　　5、谱聚类 Spectral Clustering - MapReduce

四、维数约化Dimensionality Reduction
　　1、奇异值分解 Singular Value Decomposition - single machine
　　2、Lanczos算法 Lanczos Algorithm - single machine / MapReduce
　　3、Stochastic SVD - single machine / MapReduce / Spark
　　4、主成分分析 Principal Component Analysis (via Stochastic SVD)- single machine / MapReduce

五、话题模型 Topic Models
　　1、奇异值分解 Singular Value Decomposition - single machine
　　2、Lanczos算法 Lanczos Algorithm - single machine / MapReduce
　　3、Stochastic SVD - single machine / MapReduce / Spark
　　4、主成分分析 Principal Component Analysis (via Stochastic SVD)- single machine / MapReduce


五、话题模型 Topic Models
　　1、LDA算法 Latent Dirichlet Allocation - single machine / MapReduce

六、Miscellaneous
　　1、Frequent Pattern Mining - MapReduce
　　2、Row Similarity Job - compute pairwise similarities between the rows of a matrix - MapReduce
　　3、ConcatMatrices - combine 2 matrices or vectors into a single matrix - MapReduce
　　4、Collocations - find co-locations of tokens in text - MapReduce