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Neural Designer is a data mining software based on deep learning techniques written in C++. Orange is a similar open-source project for data mining, machine learning and visualization based on scikit-learn. RapidMiner is a commercial machine learning framework implemented in Java which integrates Weka.
Speakeasy (computational environment) – numerical computational environment and programming language with many statistical and econometric analysis features; SPSS Modeler – comprehensive data mining and text analytics workbench; SPSS Statistics – comprehensive statistics package; Stata – comprehensive statistics package
Java Data Mining (JDM) is a standard Java API for developing data mining applications and tools. JDM defines an object model and Java API for data mining objects and processes. JDM enables applications to integrate data mining technology for developing predictive analytics applications and tools.
ELKI is a free tool for analyzing data, mainly focusing on finding patterns and unusual data points without needing labels. It's written in Java and aims to be fast and able to handle big datasets by using special structures. It's made for researchers and students to add their own methods and compare different algorithms easily. [2]
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...
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 ...
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While the analysis of educational data is not itself a new practice, recent advances in educational technology, including the increase in computing power and the ability to log fine-grained data about students' use of a computer-based learning environment, have led to an increased interest in developing techniques for analyzing the large amounts of data generated in educational settings.