Introduction To Seven Major Data Mining Tools

4/12/2016 12:20:05 AM

There are lots of methods such as artificial intelligence(AI), machine learning, statistic and other web crawling technologies that could be used to extract information from a large data set for further analysis and summarization. The process is called data mining.


We strongly recommend you take a look at the following seven data mining tools.




The Original non-Java version of WEKA is developed mainly for analyzing the agricultural sector data. The tool is based on the Java version. It is very complex and is used in many different applications, including data analysis and predictive modeling, visualization and algorithm. Compared with RapidMiner, its advantage is that it is free under the GNU General public license, as users can follow their own preferences to customize.


WEKA supports a variety of standard data mining tasks, including data pre-processing, data collection, data classification, regression analysis, data visualization, and feature selection. After adding series modeling which is not included now, WEKA will become more powerful.




2. Octoparse


Octoparse, a free web data extraction tool, is specifically designed for mass-gathering of various data types from almost all web pages and convert the information into structured data.

Octoparse is a WYSIWYG data collection software. It's straightforward and easy to use especially for those who don't have coding knowledge. With the help of its featured modes and visual operation pane, users can extract data from many dynamic and static websites. If you need to bulk collect large-scale of data on a regular basis, Octoparse cloud services would be your best choice.



3. RapidMiner


The tool is written in Java and provides advanced analysis techniques by  the template-based framework. The biggest advantage of the tool is that users do not need to write any code and served as a service, rather than a local software. It is worth mentioning that RapidMiner takes the top spot of list of data mining tools.


In addition to data mining, RapidMiner also provides functions such as data pre-processing and data visualization, predictive analytics and statistical modeling, evaluation and deployment capabilities. What’s more, it provides more powerful learning programs, models and algorithms which are from WEKA (an intelligent analysis environment) and R scripts as well.


RapidMiner is distributed under AGPL open source license and can be downloaded from the SourceForge. SourceForge is a centralized web server platform for developers to host static HTML content, deploy third-party open source web applications and test code. A large number of open source projects are located here, including MediaWiki which is used by Wikipedia.





When it comes to language processing tasks, nothing can beat NLTK. NLTK provides a language processing tool, including data mining, machine learning, data capture, sentiment analysis and other language processing tasks. Just install NLTK and drag a bag to your favorite tasks. Because it is written in Python, you can build applications on it and customize small tasks as well.


5. Orange


Python is very popular because it is easy to learn and powerful. If you are a Python developer and you need to find a tool for your work, then there is nothing more appropriate than Orange. Orange is a Python-based language and a powerful open source tool. It’s for both beginners and experts. 


In addition, you will definitely fall in love with this visual programming tool and Python script. It has not only the components of machine learning, but also additional biometric information and text mining. In a word, Orange contains a variety of functions for data analysis.





Data processing has three main parts: extraction, transformation and loading. KNIME Analytics Platform can do these three parts. KNIME provides users with a graphical user interface to process the data nodes. It is not only an open source comprehensive platform for data analysis and reporting, but also integrates a variety of machine learning and data mining components by its modular data pipelining concept. Besides, KNIME has gotten the attention of those who are interested in business intelligence and financial data analysis.


KNIME is based on Eclipse and written in Java. It is easy to extend and complement the plug-ins as well. Besides, its additional features can be added at any time, and large amounts of data integration modules are now included in the core version.



7. R-Programming


Imagine a scenario where I tell you that Project R, a GNU venture, is written by itself. R Programming Language (hereinafter referred to as R) is mainly written by C and FORTRAN language, and many modules of R are written by itself. R is a free software environment for statistical computing and graphics.


R is widely used in data mining, the development of statistical software and data analysis. In recent years, its ease of use and extensibility have greatly improved the popularity of R.


In addition to data , it also provides statistical and mapping technology, including linear and nonlinear modeling, classical statistical tests, time series analysis, classification, collection and so on.


This article is reprinted mainly from






Author: The Octoparse Team




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