Data mining is used wherever there is digital data available today. Notable examples of data mining can be found throughout business, medicine, science, and surveillance.
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 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 widgets with coverage of most of standard data analysis tasks and specialized add-ons for Bioorange for bioinformatics.
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-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 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 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.1 Paper Efficient “One-Row-per-Subject” Data Mart Construction for Data Mining Gerhard Svolba, PhD, SAS Austria ABSTRACT Creating a "one-row-per-subject" data mart is a fundamental task when preparing data for data mining.
Opportunities in Pharmaceutical Data Science The Promise of Big Data. The modern pharmaceutical industry is used to dealing with big numbers – big profits, big losses, big data sets. Effective data mining became critical to drug development.
Share on Facebook Share. of data mining in pharmaceutical industry. The paper presents how Data Mining discovers and extracts useful patterns from this large data to find observable patterns.
Orange Data mining: 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. Data Mining in Banking Industry Words | 12 Pages. Data mining in banking industry Describes how data mining can be used. Data mining is the process of analyzing data from multitude different perspectives and concluding it to worthwhile information.
PLENARY SPEAKERS Text and Data Mining Meets the Pharmaceutical Industry Markus Bundschus Speaks. by Steve Hardin. When you visit the doctor, you may not be thinking about text and data mining.