[Introduction to Mahout of Apache]

The Apache Mahout™ project's goal is to build an environment for quickly creating scalable performant machine learning applications.

Mahout is a very powerful data mining tool and a collection of distributed machine learning algorithms, including: the implementation of distributed collaborative filtering called Taste, classification, clustering, etc. The biggest advantage of Mahout is that it is implemented based on hadoop, which converts many algorithms that used to run on a single machine into MapReduce mode, which greatly improves the amount of data and processing performance that the algorithm can process.

 

Apache Mahout software provides three major features:

1)A simple and extensible programming environment and framework for building scalable algorithms

2)A wide variety of premade algorithms for Scala + Apache Spark, H2O, Apache Flink

3)Samsara, a vector math experimentation environment with R-like syntax which works at scale

4)On-GPU compute for performance improvements in large matrix multiplications

 

 I checked the Chinese meaning of Mahout - the person who rides the elephant, and then look at the logo of Mahout. Well, if I want to play happily with the little yellow elephant, I have to play with the elephant driver by the way...

 

Mahout currently provides tools that can be used to build a recommendation engine through the Taste library - a fast and flexible engine for CF. Taste supports user-based and item-based recommendations, and offers many recommendation options, as well as an interface for customization. Taste consists of 5 main components for manipulating users, items and preferences:

DataModel: used to store users, items and preferences

UserSimilarity: Interface for defining similarity between two users

ItemSimilarity: An interface for defining the similarity between two items

Recommender: an interface for providing recommendations

UserNeighborhood: an interface for calculating the proximity of similar users, the results of which can be used by the Recommender at any time

 

 

Machine learning algorithms implemented in Mahout:

Algorithm	Algorithm name	Chinese name
Classification algorithm	Logistic Regression	logistic regression
Bayesian	Bayesian
SVM	Support Vector Machines
Perceptron	Perceptron Algorithm
Neural Network	Neural Networks
Random Forests	random forest
Restricted Boltzmann Machines	Finite Boltzmann Machine
Clustering Algorithm	Canopy Clustering	Canopy Clustering
K-means Clustering	K-means algorithm
Fuzzy K-means	Fuzzy K-Means
Expectation Maximization	EM clustering (expectation maximization clustering)
Mean Shift Clustering	mean-shift clustering
Hierarchical Clustering	Hierarchical clustering
Dirichlet Process Clustering	Dirichlet Process Clustering
Latent Dirichlet Allocation	LDA clustering
Spectral Clustering	spectral clustering
Association Rules Mining	Parallel FP Growth Algorithm	Parallel FP Growth Algorithm
return	Locally Weighted Linear Regression	locally weighted linear regression
Dimensionality reduction/dimension reduction	Singular Value Decomposition	singular value decomposition
Principal Components Analysis	Principal component analysis
Independent Component Analysis	independent component analysis
Gaussian Discriminative Analysis	Gaussian Discriminant Analysis
Evolutionary Algorithms	Parallelized the Watchmaker framework
Recommendation/Collaborative Filtering	Non-distributed recommenders	Taste(UserCF, ItemCF, SlopeOne）
Distributed Recommenders	ItemCF
Vector similarity calculation	RowSimilarityJob	Calculate similarity between columns
VectorDistanceJob	Calculate distance between vectors
Non-Map-Reduce Algorithms	Hidden Markov Models	Hidden Markov Model
Collection method extension	Collections	Extends the Collections class of java