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Downloads of v 4.4.0:
30 Jun 2021
- KNIME.COM AG
Tags:knime data analysis analytics statistics data-mining admin
4.4.0 | Updated: 30 Jun 2021
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All Checks are Passing
3 Passing Tests
This package is likely a meta/virtual (*) or an installer (*.install) or portable (*.portable) application package.
- Meta/virtual (*) - has a dependency on the *.install or the *.portable package - it is provided for discoverability and for other packages to take a dependency on.
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This package was approved as a trusted package on 30 Jun 2021.
KNIME Analytics Platform
KNIME Analytics Platform is the open source software for creating data science applications and services. Intuitive, open, and continuously integrating new developments, KNIME makes understanding data and designing data science workflows and reusable components accessible to everyone.
Build end to end data science workflows
Create visual workflows with an intuitive, drag and drop style graphical interface, without the need for coding.
Blend tools from different domains with KNIME native nodes in a single workflow, including scripting in R & Python, machine learning, or connectors to Apache Spark.
Choose from over 2000 modules ("nodes") to build your workflow. Model each step of your analysis, control the flow of data, and ensure your work is always current.
Get up and running quickly. Select one of the hundreds of publicly available example workflows, or use the integrated workflow coach to guide you through building your workflow.
Blend data from any source
Open and combine simple text formats (CSV, PDF, XLS, JSON, XML, etc), unstructured data types (images, documents, networks, molecules, etc), or time series data.
Connect to a host of databases and data warehouses to integrate data from Oracle, Microsoft SQL, Apache Hive, and more. Load Avro, Parquet, or ORC files from HDFS, S3, or Azure.
Access and retrieve data from sources such as Twitter, AWS S3, Google Sheets, and Azure.
Shape your data
Derive statistics, including mean, quantiles, and standard deviation, or apply statistical tests to validate a hypothesis. Integrate dimension reduction, correlation analysis, and more into your workflows.
Aggregate, sort, filter, and join data either on your local machine, in-database, or in distributed big data environments.
Clean data through normalization, data type conversion, and missing value handling. Detect out of range values with outlier and anomaly detection algorithms.
Extract and select features (or construct new ones) to prepare your dataset for machine learning. Manipulate text, apply formulas on numerical data, and apply rules to filter out or mark samples.
Leverage Machine Learning and AI
Build machine learning models for classification, regression, dimension reduction, or clustering, using advanced algorithms including deep learning, tree-based methods, and logistic regression.
Optimize model performance with hyperparameter optimization, boosting, bagging, stacking, or building complex ensembles.
Validate models by applying performance metrics including Accuracy, R², AUC, and ROC. Perform cross validation to guarantee model stability.
Make predictions using validated models directly, or with industry leading PMML, including on Apache Spark.
Discover and share insights
Visualize data with classic (bar chart, scatter plot) as well as advanced charts (parallel coordinates, sunburst, network graph) and customize them to your needs.
Display summary statistics about columns in a KNIME table and filter out anything that’s irrelevant.
Export reports as PDF, Powerpoint, or other formats for presenting results to stakeholders.
Store processed data or analytics results in many common file formats or databases.
Scale execution with demands
Build workflow prototypes to explore various analysis approaches. Inspect and save intermediate results to ensure fast feedback and efficient discovery of new, creative solutions.
Scale workflow performance through in-memory streaming and multi threaded data processing.
Exercise the power of in-database processing or distributed computing on Apache Spark to further increase computation performance.
KNIME License Terms and Conditions
Please see the GPL v3, and the Additional Permissions according to Sec. 7
#Install-VirtualPackage 'knime.portable' 'knime.install'
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- knime.4.4.0.nupkg (2557d50ce02a) - ## / 62
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|KNIME 4.4.0||148||Wednesday, June 30, 2021||Approved|
|KNIME 4.3.3||101||Saturday, June 12, 2021||Approved|
|KNIME 4.3.1||308||Friday, January 29, 2021||Approved|
|KNIME 4.3.0||145||Sunday, December 6, 2020||Approved|
|KNIME 4.2.3||125||Wednesday, November 4, 2020||Approved|
|KNIME 4.2.2||172||Monday, September 28, 2020||Approved|
|KNIME 4.2.1||111||Wednesday, August 26, 2020||Approved|
|KNIME 4.2.0||179||Thursday, August 13, 2020||Approved|
|KNIME 4.1.3||237||Wednesday, May 13, 2020||Approved|
|KNIME 4.1.2||1756||Thursday, March 5, 2020||Approved|
|KNIME 4.1.1||388||Wednesday, February 5, 2020||Approved|
|KNIME 4.1.0||253||Friday, December 6, 2019||Approved|
|KNIME 4.0.2||205||Tuesday, October 1, 2019||Approved|
|KNIME 4.0.1||521||Thursday, August 15, 2019||Approved|
|KNIME 4.0.0||355||Thursday, June 27, 2019||Approved|
|KNIME 3.7.2||184||Thursday, April 18, 2019||Approved|
|KNIME 3.7.1||304||Saturday, February 2, 2019||Approved|
|KNIME 3.7.0||166||Saturday, January 19, 2019||Approved|
|KNIME 3.6.2||304||Tuesday, November 6, 2018||Approved|
|KNIME 3.6.1||525||Sunday, September 9, 2018||Approved|
|KNIME 3.6.0||222||Wednesday, July 18, 2018||Approved|
|KNIME 3.5.2||341||Tuesday, February 6, 2018||Approved|
|KNIME 3.5.1||281||Wednesday, January 3, 2018||Approved|
|KNIME 3.5.0||308||Wednesday, December 6, 2017||Approved|
|KNIME 3.4.2||295||Tuesday, November 14, 2017||Approved|
|KNIME 3.4.1||292||Monday, October 30, 2017||Approved|
|KNIME 3.4.0||351||Thursday, July 13, 2017||Approved|
|KNIME 3.3.2||359||Saturday, April 8, 2017||Approved|
|KNIME 3.3.1||397||Monday, January 16, 2017||Approved|
|KNIME 3.2.1||378||Tuesday, September 27, 2016||Approved|
|KNIME 3.1.2||400||Sunday, April 10, 2016||Approved|
|KNIME 3.1.1||322||Saturday, February 13, 2016||Approved|
|KNIME 3.1||348||Friday, January 15, 2016||Approved|
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