It is legitimately said that information is real money in the present world. Along with the change to an application based world comes the exponential development of information.

Be that as it may, the vast majority of the information is unstructured and henceforth it takes a procedure and strategy to extricate important and useful information from the unstructured data and transform it into justifiable and usable form.

This is the place data mining comes into picture. A lot of data mining tools are accessible for data mining assignments utilizing artificial intelligence, machine learning and different systems to extricate information.

Top 6 Best Open Source Data Mining Tools

Here are 6 powerful open source data mining tools accessible:

RapidMiner (earlier known as YALE)

Written in the Java Programming dialect, this device offers progressed examination through format based structures.

An extra: Users barely need to compose any code. Offered as a service, instead of a bit of local software, this data mining tool holds top position on the list of data mining tools.

Notwithstanding data mining, RapidMiner additionally gives usefulness like information preprocessing and visualization, predictive analytics and statistical modeling, evaluation, and deployment.

What makes it much more capable is that it gives learning schemes, models and algorithms from WEKA and R scripts.


The first non-Java variant of WEKA principally was produced for dissecting information from the agricultural area.

With the Java-based form, the data mining tool is exceptionally advanced and utilized as a part of a wide range of uses including visualization and algorithms for data analysis and predictive modeling.

Its free under the GNU General Public License, which is a major plus contrasted with RapidMiner, in light of the fact that clients can tweak it anyway they please.

WEKA underpins a few standard data mining undertakings, including data preprocessing, grouping, sequence, regression, visualization and feature determination.

WEKA would be all the more capable with the expansion of grouping demonstrating, which at present is excluded.


Imagine a scenario where I disclose to you that Project R, a GNU project, is written in R itself.

It’s fundamentally composed in C and Fortran. What’s more, a considerable measure of its modules are composed in R itself.

It’s a free programming dialect and programming environment for statistical computing and graphics. The R language is broadly utilized among data miners for creating statistical software and data analysis.

Convenience and extensibility has brought R’s prominence generously up lately.

Other than data mining it provides statistics and graphical techniques , including linear and nonlinear modeling, statistics tests, time-series analysis, clustering, classification, and others.


Python is gaining massive popularity since it’s basic and simple to learn yet effective.

Subsequently, with regards to searching for a tool for your work and you are a Python developer, look no more distant than Orange, a Python-based, effective and open source tool for the two beginners and specialists.

You will experience passionate feelings for this current tool’s visual programming and Python scripting.

It additionally has segments for machine learning, additional items for bioinformatics and content mining. It’s pressed with highlights for data analysis.


Data preprocessing has three principle parts:  extraction, transforming and storing.

KNIME does every one of the three. It gives you a graphical UI to take into account the collection of hubs for information handling.

It is an open source data analytics, integration and reporting platform.

KNIME additionally incorporates different segments for machine learning and data mining through its secluded information pipelining concept and has gotten the attention of business intelligence and financial data analysis.

Written in Java and based on Eclipse, KNIME is anything but difficult to stretch out and to include modules.

Extra functionalities can be included on the go. A lot of information mix modules are as of now incorporated into the core version.


With regards to language processing undertakings, nothing can beat NLTK.

NLTK gives a pool of language processing tools including datamining, machine learning, data scraping, sentiment analysis and other various language processing assignments.

You should simply install NLTK, pull a package for your most loved undertaking and you are good to go.

Since it’s written in Python, you can create applications on it, modifying it for little undertakings.



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