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MOA is the most popular open source framework for data stream mining, with a very active growing community (blog). It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.
Category
Data Analysis Software Free
Features
•Machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. •Stream mining in real time, and large scale machine learning.
License
Proprietary
Price
Contact for Pricing
Pricing
Subscription
Free Trial
Available
Users Size
Small (<50 employees), Medium (50 to 1000 Enterprise (>1001 employees)
•Machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. •Stream mining in real time, and large scale machine learning.
What are the benefits?
• Easily used with Apache Flink, Apache Storm, S4 or Samza • Handles complex knowledge workflows • Enables multi-label classification • Enables evaluation of large data sets • Enables evaluation of data stream mining
PAT Rating™
Editor Rating
Aggregated User Rating
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Ease of use
7.6
1.3
Features & Functionality
7.6
7.2
Advanced Features
7.6
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Integration
7.6
6.8
Performance
7.6
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Customer Support
7.6
9.7
Implementation
5.2
Renew & Recommend
6.1
Bottom Line
Massive Online Analysis consists of a collection of machine learning algorithms and an open source framework that enables data stream mining, regression, clustering, classification, outlier, detection, concept drift detection, and recommender systems.
7.6
Editor Rating
6.1
Aggregated User Rating
2 ratings
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Massive Online Analysis (MOA) is a framework that is open source used in stream mining of data. Massive Online Analysis consists of a collection of machine learning algorithms such as regression, classification, clustering, detection, outlier, recommender systems, and concept drift detection.
Massive Online Analysis also features tools used in evaluation of data stream mining. Massive Online Analysis is ideal for data scientists as it performs big data stream mining in real time and also perform large scale machine learning. The mining algorithms available in MOA can be extended and achieve new stream generators or evaluation measures. Massive Online Analysis features the Apache SAMOA feature.
Apache SAMOA is simply a streaming machine learning framework that is distributed and consists of an abstraction in the programming used in streaming machine learning of distributed algorithms. Apache SAMOA enables users develop new ML algorithms with directly dealing with the complex distributed stream processing engines available.
Users using Apache SAMOA have the ability of developing distributed streaming ML algorithms and execute them on multiple DSPEs. Massive Online Analysis also provides a feature known as ADAMS. ADAMS in full is Advanced Data mining And Machine Learning System.
ADAMS is aimed at facilitating quick building and maintaining data driven, reactive workflows and easy integration into business processes. This is achieved due to its flexible workflow engine. Massive Online Analysis also features the MEKA project that facilitates open source implementation of methods used for multi-label evaluation and classification.
Massive Online Analysis provides real time analytics for streams of data makes it ideal for data scientists to be able to processes large data sets using the provided tools and solutions.
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