Six powerful open-source data mining tools

In today's big data era, data is money. With the transition to an application-based domain, data shows exponential growth. However, 80% of the data is unstructured, so it requires a program and method to extract useful information and convert it into an understandable and available structured form.

A large number of tools are available in data mining, such as artificial intelligence, machine learning, and other technologies.

We recommend six data mining tools as follows:

1. WEKA

The native non-Java version of WEKA is mainly developed to analyze data in the agricultural field. The tool is based on the Java version and is very complex and applied in many different applications, including data analysis and prediction modeling visualization and algorithms. Compared with rapidminer, it is free of charge under the GNU General Public License, because users can choose to customize according to their preferences.

WEKA supports a variety of standard data mining tasks, including data preprocessing, collection, classification, regression analysis, visualization, and feature selection. After adding a sequence modeling, WEKA will become more powerful, but not yet.

2. rapidminer

This tool is written in Java and provides advanced analysis technology through a template-based framework. The biggest benefit of this tool is that you do not need to write any code. It is provided as a service rather than a local software. It is worth mentioning that this tool is on the top of the data mining tool list.

In addition to data mining, rapidminer also provides functions such as data preprocessing and visualization, predictive analysis and statistical modeling, evaluation and deployment. What's more, it also provides learning solutions, models, and algorithms from WEKA (an Intelligent Analysis Environment) and R scripts.

Rapidminer is distributed under the open source license of agpl and can be downloaded from SourceForge. SourceForge is a centralized place for developers to conduct development and management. A large number of open-source projects are settled here, including mediawiki used by Wikipedia.

3. nltk

When it comes to language processing tasks, nothing can beat nltk. Nltk provides a language processing tool, including data mining, machine learning, data capturing, sentiment analysis, and other language processing tasks.

All you need to do is install nltk and drag a package to your favorite task. Then you can do other things. Because it is written in Python, you can create an application on it and customize its small tasks.

4. Orange

Python is popular because it is easy to learn and powerful. If you are a python developer and need to find a working tool, it is no more suitable than orange. It is a powerful open-source tool based on the Python language and is applicable to beginners and experts.

In addition, you will surely fall in love with Visual Programming and Python scripts of this tool. It not only has machine learning components, but also has biological information and Text Mining. It can be said that it is full of various functions of data analysis.

5. knime

Data processing consists of three parts: extraction, conversion, and loading. All three knime can do this. Knime provides a graphical user interface for you to process data nodes. It is an open-source data analysis, report, and comprehensive platform. It also integrates various machine learning components and data mining through its modular data streamline concept, and attracted the attention of business intelligence and financial data analysis.

Knime is based on Eclipse and written in Java, and is easy to expand and supplement plug-ins. Its additional functions can be added at any time, and a large number of data integration modules are included in the core version.

6. R-Programming

What do you think if I tell you that a GNU project is compiled by R (R-Programming? It is mainly written by C and FORTRAN languages, and many modules are written by R. It is a free software for Statistical Computing and drawing for programming languages and software environments.

The r language is widely used in data mining and development of statistical software and data analysis. In recent years, ease of use and scalability have greatly increased the popularity of R. In addition to data, it also provides statistics and drawing technologies, including linear and nonlinear modeling, classic statistical testing, time series analysis, classification, collection, and so on.

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Six powerful open-source data mining tools