In the era of the Internet to the era of big data, then data is equal to money. As the transition to an application-based domain, the data showed an exponential growth. However, 80% of the data is unstructured, so it requires a program and method to extract useful information from it and convert it into an understandable, usable structured form.
In the data mining process, there are a large number of tools available, such as artificial intelligence, machine learning, and other technologies to extract data.
With the explosive growth of data volume, we need some effective tools for data mining, which helps us to more easily find relationships, clusters, patterns, classification information, etc. from a huge data set. Using these tools can help us make the most accurate decisions and get more out of our business.
The following small series summarizes 10 best data mining tools for everyone, which can help you analyze big data from various angles and make correct business decisions through data:
1. RapidMinerRapidMiner is one of the most popular free data mining tools. It is an open source data mining software written in Java language that provides implementations of scalable data analysis and mining algorithms designed to help developers more easily and quickly. Create a smart app. The biggest benefit of this tool is that the user does not need to write any code. It is offered as a service, not a local software.
In addition to data mining, RapidMiner provides features such as data pre-processing and visualization, predictive analytics and statistical modeling, evaluation and deployment.
RapidMiner also has some useful extensions that can be used to build recommendation systems and comment mining systems. An extension package is the recommended system extension package rmx_irbrecommender-ANY-5.0.4.jar, which enables direct implementation of content-based and collaborative filtering. Recommended system. Another extension package is the information extraction extension package rapidminer-InformaTIon-ExtracTIon-1.0.2.jar, which can be used to extract features and viewpoint words. If combined with the text classification function provided by RapidMiner, a comment mining prototype system should be implemented. .
SAS originated at North Carolina State University. In 1976, SAS software was separated from the school and entered the company. Users can use SAS data mining business software to explore patterns of data sets, and their descriptive and predictive models provide the basis for users to understand data more deeply.
Users don't need to write any code, they provide an easy-to-use GUI and provide automated tools for data processing, clustering, and finalization, from which users can get the best results and make the right decisions. Because it is a commercial data mining software, it contains many high-end tools, including automation, dense image algorithms, modeling, data visualization, and more.
WEKA is a very complex data mining tool whose native non-Java version was developed primarily for the analysis of agricultural data. Based on the Java version, the tool supports a variety of standard data mining tasks, including data preprocessing, collection, classification, regression analysis, visualization, and feature selection.
The advantage over Rapid Miner is that it is free under the GNU General Public License because users can choose to customize it to their liking.
Advanced users can call their analysis components through Java programming and the command line. At the same time, Weka also provides a graphical interface for the average user, called Weka KnowledgeFlow Environment and Weka Explorer. In addition, users can find many extensions in the Weka forum, such as text mining, visualization, grid computing and more. Many other open source data mining software also supports calling Weka's analysis capabilities.
R software is another popular GNU open source data mining tool. It is mainly written by C language and FORTRAN language. It is a free software for statistical calculation and drawing of programming language and software environment.
In addition to providing data mining and analysis capabilities for scientists, researchers, and students, it also provides statistical and mapping techniques, including linear and nonlinear modeling, classical statistical testing, time series analysis, classification, collection, and more.
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