Top 10 data mining tools most needed for big data

First, we need to understand what is data mining? The official definition is as follows: Data mining is also known as data mining and data mining. It is a step in Knowledge-Discovery in Databases (KDD), which generally refers to the process of searching for information hidden in it from a large amount of data through an algorithm. Data mining is often associated with computer science and achieves these goals through statistics, online analytical processing, intelligence retrieval, machine learning, expert systems (reliant on past rules of thumb), and pattern recognition.

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:

TOP10 data mining tool

1. RapidMiner

RapidMiner 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. .

Download address: https://rapidminer.com/

2. SAS Data Mining (SAS Data Mining Software)

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.

Download address: https://www.sas.com/

3. WEKA

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 ordinary users, 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.

Download address: http://www.cs.waikato.ac.nz/ml/weka/

4. Software – R

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.

Download address: http://www.rdatamining.com/package

5. Orange data mining software

Orange is an open source data mining and machine learning tool. Its graphical environment is called OrangeCanvas. Users can place analysis widgets on the canvas and then connect the controls to form the mining process. In addition to the interface-friendly and easy-to-use advantages, Orange's strength lies in providing a large number of visualization methods, which can display a variety of graphical representations of data and models, and intelligently search for appropriate visualizations to support interactive exploration of data.

In addition, it includes a complete set of components for data preprocessing and provides data accounting, transition, modeling, pattern evaluation and exploration capabilities.

Orange's weakness is that traditional statistical analysis capabilities are not strong, do not support statistical testing, and reporting capabilities are limited. Orange's underlying core is also written in C++, while allowing users to extend development using the Python scripting language.

Download address: orange.biolab.si

6. KNIME

KNIME (Konstanz Information Miner) is an open source data analysis, reporting and synthesis platform written in Java based on Eclipse. It has all the data mining tools needed for data extraction, integration, processing, analysis, transformation and loading. In addition, it has a graphical user interface that helps users easily connect nodes for data processing.

It combines the various components of data mining and machine learning and is very helpful for business intelligence and financial data analysis. In addition, users can easily extend KNIME by adding additional features at any time.

Download address: https://www.knime.org/

7. NLTK

The NLTK (Natural Language Tool Kit) is best suited for language processing tasks because it provides a language processing tool that includes various language processing tasks such as data mining, machine learning, data grabbing, and sentiment analysis. All you have to do is install NLTK, then drag and drop a package into your favorite task and you can do other things. Because it is written in Python, you can build applications on it and customize its small tasks.

Download address: http://www.nltk.org/

8. JHepWork

Designed for scientists, engineers and students, jHepWork is a free open source data analysis framework that uses open source libraries to create a data analysis environment and provides a rich user interface to compete with paid software. . It is mainly used for 2D and 3D mapping for scientific computing, and contains mathematical science libraries, random numbers, and other data mining algorithms implemented in Java. jHepWork is based on a high-level programming language, Jython. Of course, Java code can also be used to call jHepWork's math and graphics libraries.

Download address: https://sourceforge.net/projects/jhepwork/

9. Pentaho

Pentaho provides a comprehensive platform for data integration, business analytics and big data processing. With this commercial tool, you can easily mix data from a variety of sources, and by analyzing business data, you can provide the right information for future decisions.

Download address: http://www.pentaho.com/

10. Tanagra

Tanagra is a data mining software developed for academic and research purposes and is completely free. It uses a graphical interface for data mining software that uses a tree structure similar to Windows Explorer to organize the analysis components. Tanagra lacks advanced visualization capabilities, but its strengths are statistical analysis, providing a wide range of parametric and non-parametric methods. At the same time, its feature selection methods are also many.

Download address: eric.univ-lyon2.fr/~ricco/tanagra/en/tanagra.html