- Data Access
- Files
- Read
- Read URL
- Write
- Database
- NoSQL
- Cassandra
- MongoDB
- Solr
- Applications
- Salesforce
- Mozenda
- Qlik
- Splunk
- Cloud Storage
- Amazon S3
- Azure Blob Storage
- Delete Azure Blob Storage Resource
- Azure Data Lake Storage Gen1
- Delete Azure Data Lake Storage Resource
- Loop Azure Data Lake Storage
- Read Azure Data Lake Storage
- Write Azure Data Lake Storage
- Azure Data Lake Storage Gen2
- Delete Azure Data Lake Storage Gen2 Resource
- Loop Azure Data Lake Storage Gen2
- Read Azure Data Lake Storage Gen2
- Write Azure Data Lake Storage Gen2
- Dropbox
- Google Storage
- Blending
- Attributes
- Names & Roles
- Types
- Numerical to Date
- Selection
- Generation
- Generate Gaussians
- Generate Item Set Indicators
- Generate Weight (LPR)
- Examples
- Filter
- Sampling
- Sample (Model-Based)
- Sort
- Sort by Pareto Rank
- Table
- Grouping
- Rotation
- Joins
- Cartesian Product
- Values
- Add
- Cleansing
- Normalization
- De-Normalize
- Binning
- Missing
- Remove Unused Values
- Duplicates
- Outliers
- Dimensionality Reduction
- Modeling
- Predictive
- Ungroup Models
- Update Model
- Lazy
- Bayesian
- Trees
- Rules
- Single Rule Induction
- Single Rule Induction (Single Attribute)
- Neural Nets
- AutoMLP
- Functions
- Seemingly Unrelated Regression
- Logistic Regression
- Support Vector Machines
- Support Vector Machine (Linear)
- Discriminant Analysis
- Ensembles
- Additive Regression
- Find Threshold (Meta)
- Hierarchical Classification
- Relative Regression
- Subgroup Discovery (Meta)
- Transformed Regression
- Segmentation
- X-Means
- k-Means (fast)
- Associations
- Item Sets to Data
- Correlations
- Rainflow Matrix
- Transition Graph
- Transition Matrix
- Similarities
- Feature Weights
- Optimization
- Parameters
- Feature Selection
- Optimize Selection (Weight-Guided)
- Feature Generation
- Optimize by Generation (AGA)
- Optimize by Generation (Evolutionary Aggregation)
- Feature Weighting
- Optimize Weights (Backward)
- Optimize Weights (PSO)
- Time Series
- Transformation
- Decomposition
- Feature Extraction
- Windowing
- Forecasting
- Validation
- Utility
- Scoring
- Confidences
- Generate Prediction
- Generate Prediction Ranking
- Rescale Confidences
- Select Recall
- Validation
- Performance
- Performance (User-Based)
- Predictive
- Performance (Support Vector Count)
- Segmentation
- Significance Tests
- Visual
- Utility
- Scripting
- Process Control
- Loops
- Loop Data Fractions
- Loop Repository
- Loop Until
- Loop Zip-File Entries
- Branches
- Collections
- Average
- Exceptions
- Macros
- Unset Macro
- Files
- Open File
- Write File
- Annotations
- Logging
- Clear Log
- Log to Weights
- Print to Console
- Data Anonymization
- Random Data Generation
- Generate Churn Data
- Generate Massive Data
- Generate Team Profit Data
- Generate Transfer Data
- Generate Up-Selling Data
- Misc
- Delay
Developer(s) | RapidMiner |
---|---|
Initial release | 2006; 15 years ago |
Stable release | |
Operating system | Cross-platform |
Type | Data science, machine learning, predictive analytics |
License | Professional and Enterprise Editions are Proprietary; Free Edition (10,000 rows and 1 logical processor limit) is available as AGPL |
Website | rapidminer.com |
Rapidminer
Get even more out of RapidMiner with Extensions. Extensions add new functionality to RapidMiner, like text mining, web crawling, or integration with Python and R.
RapidMiner is a data science software platform developed by the company of the same name that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics. It is used for business and commercial applications as well as for research, education, training, rapid prototyping, and application development and supports all steps of the machine learning process including data preparation, results visualization, model validation and optimization.[1] RapidMiner is developed on an open core model.
History[edit]
Rapidminer Auc
RapidMiner Conferenceでは、そのような期待に応えるべく、RapidMinerの最新の情報や連携ソリューションをご紹介します。また、動画配信形式でRapidMinerハンズオンもご提供させて頂きますので、自分自身でRapidMinerを起動しながらご参加頂くことも可能です。. Getting started with RapidMiner Studio Probably the best way to learn how to use RapidMiner Studio is the hands-on approach: Download RapidMiner Studio, and study the bundled tutorials. Once you've looked at the tutorials, follow one of the suggestions provided on the Start Page. RapidMiner is a data science software platform developed by the company of the same name that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.
RapidMiner, formerly known as YALE (Yet Another Learning Environment), was developed starting in 2001 by Ralf Klinkenberg, Ingo Mierswa, and Simon Fischer at the Artificial Intelligence Unit of the Technical University of Dortmund.[2] Starting in 2006, its development was driven by Rapid-I, a company founded by Ingo Mierswa and Ralf Klinkenberg in the same year.[3] In 2007, the name of the software was changed from YALE to RapidMiner. In 2013, the company rebranded from Rapid-I to RapidMiner.[4]
Description[edit]
RapidMiner uses a client/server model with the server offered either on-premises or in public or private cloud infrastructures.
According to Bloor Research, RapidMiner provides 99% of an advanced analytical solution through template-based frameworks that speed delivery and reduce errors by nearly[peacock term] eliminating the need to write code. RapidMiner provides data mining and machine learning procedures including: data loading and transformation (ETL), data preprocessing and visualization, predictive analytics and statistical modeling, evaluation, and deployment. RapidMiner is written in the Java programming language. RapidMiner provides a GUI to design and execute analytical workflows. Those workflows are called “Processes” in RapidMiner and they consist of multiple “Operators”. Each operator performs a single task within the process, and the output of each operator forms the input of the next one. Alternatively, the engine can be called from other programs or used as an API. Individual functions can be called from the command line. RapidMiner provides learning schemes, models and algorithms and can be extended using R and Python scripts.[5]
RapidMiner functionality can be extended with additional plugins which are made available via RapidMiner Marketplace. The RapidMiner Marketplace provides a platform for developers to create data analysis algorithms and publish them to the community.[6]
The RapidMiner Studio Free Edition, which is limited to one logical processor and 10,000 data rows, is available under the AGPL license,[7]
Products[edit]
- RapidMiner Studio
- RapidMiner Auto Model
- RapidMiner Turbo Prep
- RapidMiner Go
- RapidMiner Server
- RapidMiner Radoop
Adoption[edit]
Rapidminer Tutorial
In 2019, Gartner placed RapidMiner in the leader quadrant of its Magic Quadrant for Data Science & Machine Learning Platforms for the sixth year in a row.[8] The report noted that RapidMiner provides deep and broad modeling capabilities for automated end-to-end model development. In the 2018 annual software poll, KDnuggets readers voted RapidMiner as one of the most popular data analytics software with the poll’s respondents citing the software package as the tool they use.[9] RapidMiner has received millions of total downloads and has over 400,000 users including BMW, Intel, Cisco, GE, and Samsung as paying customers. RapidMiner claims to be the market leader in the software for data science platforms against competitors such as SAS and IBM.[10]
Developer[edit]
About 50 developers worldwide participate in the development of the open source RapidMiner with the majority of the contributors being employees of RapidMiner.[11] The company that develops RapidMiner received a $16 million Series C funding with participation from venture capital firms Nokia Growth Partners, Ascent Venture Partners, Longworth Venture Partners, Earlybird Venture Capital and OpenOcean. OpenOcean partner Michael 'Monty' Widenius is a founder of MySQL.[citation needed]
Rapidminer Cnn
References[edit]
- ^Markus Hofmann, Ralf Klinkenberg, “RapidMiner: Data Mining Use Cases and Business Analytics Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series),” CRC Press, October 25, 2013.
- ^Guido Deutsch, “RapidMiner from Rapid-I at CeBIT 2010,” Data Mining Blog, March 18, 2010.
- ^“Interview with RapidMiner's Ingo Mierswa, Ralf Klinkenberg”, KDnuggets, February, 2010.
- ^“German Predictive Analytics Startup Rapid-I Rebrands As RapidMiner”, TechCrunch, November 4, 2013.
- ^David Norris, “RapidMiner - a potential game changer,” Bloor Research, November 13, 2013.
- ^Ajay Ohri, “Interview with Rapid-I Ingo Mierswa and Simon Fischer,” KDnuggets, August 2011.
- ^RapidMiner Embraces its Community and Open Source Culture Delivering Get-More-Open-Core Predictive Analytics, September 1, 2015.
- ^'Gartner Magic Quadrant for Data Science and Machine Learning Platforms'. Gartner. Retrieved 25 October 2020.
- ^'Python eats away at R: Top Software for Analytics, Data Science, Machine Learning in 2018: Trends and Analysis'. www.kdnuggets.com. Retrieved 2018-10-05.
- ^Ingrid Lunden, “German Predictive Analytics Startup Rapid-I Rebrands As RapidMiner, Takes $5M From Open Ocean, Earlybird To Tackle The U.S. Market,” TechCrunch, November 4, 2013.
- ^Evan Quinn, “Is Rapid-I the Hidden Giant of Analytics?,” QuinnSight Research, June 17, 2013.