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Orange Data mining
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Orange Data mining

Overview
Synopsis

Orange is an open source data visualization and analysis tool, where data mining is done through visual programming or Python scripting. The tool has components for machine learning, add-ons for bioinformatics and text mining and it is packed with features for data analytics.

Category

Data Mining Software

Features

• Open Source
• Interactive Data Visualization
• Visual Programming
• Supports Hands-on Training and Visual Illustrations
• Add-ons Extend Functionality

License

Open Source Software

Price

Free

Pricing

Subscription

Free Trial

Available

Users Size

Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)

Website
What is best ?

• Open Source
• Interactive Data Visualization
• Visual Programming
• Supports Hands-on Training and Visual Illustrations
• Add-ons Extend Functionality

What are the benefits ?

•For everyone- beginners and professionals
•Execute simple and complex data analysis
•Create beautiful and interesting graphics
•Use in a data analysis lecture
•Access external functions for advanced analysis

Rating
Our Rating
User Rating
Ease of use
9.6
8.6
Features & Functionality
9.5
8.2
Advanced Features
9.5
8.3
Integration
9.4
8.1
Customer Support
9.6
8.3
Performance
9.4
8.5
Training
8.2
Implementation
8.1
Renew & Recommend
8.2
Bottom Line

Orange is an open source data visualization and analysis tool, where data mining is done through visual programming or Python scripting. The tool has components for machine learning, add-ons for bioinformatics and text mining and it is packed with features for data analytics.

9.5
Our Rating
8.3
User Rating
1 rating
You have rated this

Orange is an open source data visualization and analysis tool. Orange is developed at the Bioinformatics Laboratory at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia, along with open source community. Data mining is done through visual programming or Python scripting. The tool has components for machine learning, add-ons for bioinformatics and text mining and it is packed with features for data analytics. Orange is a Python library. Python scripts can run in a terminal window, integrated environments like PyCharm and PythonWin, or shells like iPython. Orange consists of a canvas interface onto which the user places widgets and creates a data analysis workflow. Widgets offer basic functionalities such as reading the data, showing a data table, selecting features, training predictors, comparing learning algorithms, visualizing data elements, etc. The user can interactively explore visualizations or feed the selected subset into other widgets.

Orange Data mining

Orange-Visualization of interactions of genetic pathways

Orange-Visualization of interactions of genetic pathways

In Orange, data analysis process can be designed through visual programming. Orange remembers the choices, suggests most frequently used combinations. Orange has features for different visualizations, such as scatterplots, bar charts, trees, to dendrograms, networks and heatmaps. By combining the various widgets the design of data analytics framework can be done. There are over 100 widgets with coverage of most of standard data analysis tasks and specialized add-ons for Bioorange for bioinformatics.

 

Orange- Tree view of Orange widgets

Orange- Tree view of Orange widgets

Own widgets, can be developed and the scripting interface can be extended to create self contained add-ons, integrating with the rest of Orange, allowing components and code reuse. Orange runs on Windows, Mac OS X, and variety of Linux operating systems.

Orange-Data exploration by construction of analysis schema

Orange-Data exploration by construction of analysis schema

Orange comes with mutliple classification and regression algorithms. New ones can be build or there are features to wrap existing learners and add some preprocessing to construct new variants.

Orange-Explorative analysis and classification trees

Orange-Explorative analysis and classification trees

Orange can read files in native and other data formats. Orange is devoted to machine learning methods for classification, or supervised data mining. Classification uses two types of objects: learners and classifiers. Learners consider class-labeled data and return a classifier. Regression methods in Orange are very similar to classification. Both intended for supervised data mining, they require class-labeled data. Learning of ensembles combines the predictions of separate models to gain in accuracy. The models may come from different training data samples, or may use different learners on the same data sets. Learners may also be diversified by changing their parameter sets. In Orange, ensembles are simply wrappers around learners. They behave just like any other learner. Given the data, they return models that can predict the outcome for any data instance.

Orange-Survey plot

Orange-Survey plot

Orange
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1 Reviews
  • Katheryn Lipszyc
    April 26, 2017 at 12:01 pm

    Complex analytical functions

    What is best ?

    It can also be used to test new machine learning algorithms. Orange is valuable in the classroom as it can be utilized in the teaching of machine learning and data mining .

    Company size

    Medium (50 to 1000)

    User Role

    Super User

    User Industry

    Financial services

    Rating
    Ease of use8.6

    Features & Functionality8.2

    Orange is an open-source software package supported on MacOS, Windows and Linux Operating systems and serves as a platform for experiment selection, sytems of recommendation as well as predictive modelling.

    Advanced Features8.3

    Orange also has add-ons for specialist tasks such as fusing data sets, teaching machine learning concepts, analysis of geospatial data, bioinformatics, image analysis, network analysis, time series analysis, natural language processing and text mining.

    Integration8.1

    Customer Support8.3

    Performance8.5

    Training 8.2

    Implementation8.1

    Renew & Recommend8.2

    ADDITIONAL INFORMATION
    Built-in functionalities are numerous. Some of them include: reading of data, input of data into data tables, selection of feature, training predictors, comparing different learning algorithms, and visualization of data elements. The Orange software contains widgets for data input, widgets for common visualization which produce very interactive graphics, widgets for classification, widgets for regression, widgets for evaluation and those for other complex analytical functions.

Ease of use
Features & Functionality
Advanced Features
Integration
Customer Support
Performance
Training
Implementation
Renew & Recommend

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