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This article introduces the knowledge about "what algorithms are implemented in Apache Mahout". In the actual case operation process, many people will encounter such difficulties. Next, let Xiaobian lead you to learn how to deal with these situations! I hope you can read carefully and learn something!

Apache Mahout is an open source project owned by Apache Software Foundation (ASF), which provides some scalable implementations of classic algorithms in the field of machine learning, aiming to help developers create intelligent applications more easily and quickly, and the latest version of Mahout also adds support for Apache Hadoop, so that these algorithms can run more efficiently in cloud computing environments.

The machine learning algorithms implemented in Mahout are shown in the table below:

algorithm class

algorithm name

Chinese name

classification algorithm

Logistic Regression

logistic regression

Bayesian

Bayesian

SVM

support vector machine

Perceptron

perceptron algorithm

Neural Network

neural network

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 rule mining

Parallel FP Growth Algorithm

Parallel FP Growth Algorithm

regression

Locally Weighted Linear Regression

locally weighted linear regression

Dimension 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 algorithm

Parallelized 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 the distance between vectors

Non-Map-Reduce algorithm

Hidden Markov Models

hidden Markov model

set method extension

Collections

Extended java Collections class

Mahout's biggest advantage is that it is based on hadoop implementation, transforming many algorithms previously running on a single machine into MapReduce mode, which greatly improves the amount of data that the algorithm can process and the processing performance.

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