Top Tools / December 16, 2022

The world's biggest online directory of resources and tools for startups and the most upvoted product on ProductHunt History.

Top 28 Data Mining Tools

Today, every firm relies on the characteristics of data mining tools and data extraction software to plan business decisions and dramatically reduce costs. Different methodologies are required for various data mining technologies. Some tools do not require any programming knowledge, while others may demand only a basic understanding of coding. There are numerous free and interesting data mining tools accessible on the market. The majority of these tools don't require any programming and can be used with a simple drag-and-drop interface.

In this top tools list, we have compiled the top 28 data mining tools along with their features and pricing for you to choose from.

1. Viscovery

Viscovery is a workflow-oriented software suite for explorative data mining and predictive modeling based on self-organizing maps (SOM) and multivariate statistics.

Intuitive user advice, mature implementation, and a persistent focus on applications are all strengths of the system. Benefit from Viscovery's visual data representation's unrivaled compactness and elegance.

Key Features:

  • It workflows from Viscovery Platform for goal-oriented operating in a project setting.

  • Workflows that are dedicated to providing targeted navigation.

  • Steps in the procedure are clearly defined, with default settings that have been verified to work.

  • Workflow branching allows model variations to be created.

  • Integrated documentation and annotation functions.

  • To make it easier to use, there is a variety of handling tools.


You can request a quote on their website.

2. GGplot2

GGplot2 , one of the most used R packages, is a data visualization tool. Users can use this tool to change components inside a plot at a high level of abstraction. It also allows users to create practically any form of graph and increase the quality and beauty of visuals.

Key Features:

  • Give ggplot2 the data, tell it how to map variables to aesthetics and which graphical primitives to use, and it'll do the rest.

  • Layers, scales, faceting standards, and coordinate systems can all be added to the mix.

  • ggplot2 has been around for over a decade and has been used to create millions of plots by hundreds of thousands of individuals. As a result, ggplot2 changes quite little in general.


This is a free tool.

3. Civis Platform

Civis Platform serves as the foundation for a living data science system, allowing you to confidently make educated decisions. The cloud-based Civis Platform was created with data scientists and decision-makers in mind, allowing your team to work easily and identify solutions faster—closing the loop on measurement, activation, and attribution.

Key Features:

  • You can import, de-duplicate, unify, and enrich your data with tools like Civis Data and the Identity Resolution API, making it comprehensive and ready for analysis.

  • Use our query tool to explore your data, use CivisML to develop models in Jupyter Notebooks, and use our Github connection to collaborate across teams.

  • Create a script, combine many scripts or jobs into a process, then schedule the workflow to execute.

  • Transform your research and models into apps that run on a scalable, production-ready architecture. Instantly deploy your models as APIs, or use reports to distribute results to stakeholders.


You can request a quote on their website.

4. Rattle

Rattle (R Analytical Tool To Learn Easily) is a free and open-source GUI for beginners who wish to execute data mining activities with a single point-and-click. It receives 10,000 to 20,000 downloads per month. All Rattle interactions are saved as an R script, which may be run without having to use the Rattle GUI. By creating your initial models in the Rattle interface, you can also use it as a tool for learning and perfecting your R programming skills.

Key Features:

  • Togaware offers Rattle as an open-source project that can be downloaded for free.

  • Rattle can be used to deliver data mining tasks by itself, thanks to a simple and logical graphical user interface based on Gnome.

  • Rattle also gives you a taste of advanced data mining with R, an open-source and free statistical language.

  • The goal is to create an easy-to-use interface that walks you through the basic phases of data mining while also demonstrating the R code needed to do so.


This is a free tool.

5. PolyAnalyst

PolyAnalyst, by MegaComputer, is the industry standard for extracting usable knowledge from large amounts of unstructured and unstructured data. PolyAnalyst is the tool of choice for turning data into useful business insight, regardless of the data source, problem, or skill level.

Key Features:

  • Data from practically any source can be accessed, and data from several sources can be combined.

  • Powerful data cleansing processes, such as imputing missing values and rectifying spelling errors, can help you clean up your data.

  • Select from a wide range of statistical and machine-learning techniques, as well as a number of cutting-edge natural language processing tools.

  • Create eye-catching reports that sum up and express your knowledge.


You can request a quote on their website.

6. MatPlotLib

MatPlotLib is a fantastic Python data visualisation library. This library enables the use of interactive figures as well as the creation of high-quality plots (such as histograms, scatter plots, 3D plots, and image plots) that may be altered later (styles, axes properties, font, etc.). It is a Python package that allows you to create static, animated, and interactive visualisations.

Key Features:

  • With just a few lines of code, you can create publication-quality graphs.

  • Use interactive graphs that can be zoomed in, panned, and updated.

  • Line styles, font properties, and axis properties can all be customized to your liking.

  • A variety of file formats and interactive environments are available for export and embedding.

  • Examine the custom functionality offered by third-party programs.

  • Many external learning materials are available to help you learn more about Matplotlib.


This is a free tool.

7. Apache Spark

Apache Spark is a multi-threaded, in-memory, distributed, and iterative open-source analytics engine that promises a clean, simple, and enjoyable experience while developing parallel programs. Over 3000 enterprises, including prominent players like Amazon, Visa, Oracle, Hortonworks, Verizon, and Cisco, use it because of its ease of use, speed, scalability, and high-performance analysis on massive datasets.

Spark is a visually appealing API that supports Python, Java, and R, among other programming languages. Spark should be on your to-do list if you want to work with Big Data or the Internet of Things.

Key Features:

  • Using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine, Apache Spark provides great performance for both batch and streaming data.

  • Over 80 high-level operators are available in Spark, making it simple to create parallel apps. It's also interactively accessible through the Scala, Python, R, and SQL shells.

  • SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming are just a few of the libraries powered by Spark. These libraries can be used together in the same application without any issues.


This is a free tool.

8. Analytic Solver

Analystic Solver is a point-and-click utility that is available for free. In your browser, you may perform risk analysis and prescriptive analytics. It provides full-fledged data mining positions. It's backward compatible with Excel Solver and can handle any type and size of traditional optimization issue (without uncertainty). Unlike other optimization applications, it evaluates your model structure algebraically and makes the most of your computer's numerous cores.

Key Features:

  • You can solve nonlinear models 10 times larger and linear models 40 times larger than the Excel Solver, and you can use Solver Engines to handle up to millions of variables.

  • It's packed with features, including 60 probability distributions, compound distributions, automatic distribution fitting, rank-order, and copula-based correlation, 80 statistics, risk measures, and Six Sigma functions, multiple parameterized simulations, and more.

  • You may sample data from SQL databases, Power Pivot, and Apache Spark, graphically explore your data, clean and transform data, and construct, test, and deploy time series forecasting and data mining models.


This is a free tool.

9. TensorFlow

TensorFlow is an open-source Python library for machine learning that was created by the Google Brain Team. It focuses on deep neural networks and can be used to develop deep learning models. TensorFlow not only has a broad ecosystem of tools, but it also contains other libraries and a large community where developers can ask questions and exchange ideas. TensorFlow, although being a Python library, added a R interface from RStudio to the TensorFlow API in 2017.

Key Features:

  • Build and train machine learning models quickly with Keras' straightforward high-level APIs, which feature eager execution for quick model iteration and debugging.

  • No matter what language you use, you can easily train and deploy models in the cloud, on-prem, in the browser, or on-device.

  • A simple and adaptable design that allows new ideas to move more quickly from concept to code, state-of-the-art models, and publication.


This is a free tool.

10. H2O

H2O is an end-to-end platform that helps businesses construct world-class Artificial IntelligenceI models and apps quickly. There are no user restrictions, allowing everyone in the organisation to innovate and apply AI to improve decision-making, expedite processes, minimise risk, and personalise consumer experiences.

Key Features:

  • Every step of the data science lifecycle, including data visualisation, feature engineering, algorithm selection, and hyper-parameter tweaking, can be automated with H2O AI Hybrid Cloud.

  • Over a dozen analyses are produced by the H2O AI Hybrid Cloud to assist enterprises in validating models, removing bias, and interpreting the results. Customers can explore the results using the user interface, which features sophisticated visuals.

  • The H2O AI Hybrid Cloud trains models quickly and efficiently on both purpose-built and commodity hardware.


You can request a quote on their website.

11. Advanced Miner

Advanced Miner makes data processing, analysis, and modelling easier. You can explore various types of data using its user-friendly workflow interface. The easy-to-use workflow interface allows you to explore all of your data and more. The drag-and-drop technique can be used to define complex analytical processes in a simple manner.

Key Features:

  • Data extraction and storage from/to various database systems, files, and data transformations.

  • Sampling, joining datasets, and dividing data are just a few examples of data operations.

  • Building well-known statistical models, clustering analysis, variable importance analysis, model quality evaluation, and model comparison are all things that Data Mining models can do (LIFT, ROC, K-S, Confusion Matrix).

  • Integration of models with external IT applications (using scoring code).


You can request a quote on their website.

12. Pytorch

Pytorch is a deep learning framework and Python package based on the Torch library. It was created by Facebook's AI Research Lab (FAIR), and its deep neural networks functionality has made it a well-known Data Science tool. It allows users to construct a whole neural network using data mining stages such as loading data, preprocessing data, defining a model, training it, and evaluating it.

Torch also allows for quick array computations thanks to its powerful GPU acceleration. This library was recently made available to R, in September 2020. Torch, torchvision, torchaudio, and other extensions are part of the torch for R ecosystem.

Key Features:

  • TorchScript lets you smoothly switch between eager and graph modes, while TorchServe helps you get to production faster.

  • The torch. distributed backend enables scalable distributed training and performance improvement in research and production.

  • PyTorch is extended by a vast ecosystem of tools and modules that help development in computer vision, natural language processing, and other fields.

  • PyTorch is well-supported on major cloud platforms, making development and scalability simple.


This is a free tool.

13. IBM SPSS Modeler

IBM SPSS Modeler is one of the top data mining tools that helps organisations reduce time to value significantly by allowing data scientists to complete operational jobs faster. It's used by companies all over the world for data preparation and discovery, predictive analytics, model management and deployment, and machine learning to monetize data assets.

Key Features:

  • Begin with data science and machine learning methods that are GUI-based.

  • Empower coders, analysts, and non-coders.

  • Begin small and work your way up with enterprise-level security and control.

  • Use open source innovation, such as R or Python, to your benefit.

  • Consider it a hybrid solution that includes on-premises and public or private cloud computing.


Packages start at $499 per month.

14. Panopticon

Panopticon, by Altair, provides a drag-and-drop interface which allows business users, analysts, and engineers – the people closest to the action — to create, modify, and deploy powerful data visualisation and stream processing applications.

They can connect to almost any data source, including big data sources, SQL and NoSQL databases, and message queues; write complicated event processing programs, and create visual user interfaces that offer them the perspectives they need to make informed decisions based on huge volumes of data.

Key Features:

  • Create and securely distribute dashboards and reporting screens across the organisation.

  • Respond rapidly to changing company needs, lower risk and costs, and swiftly deploy new applications and dashboards.

  • Automatically create smart dashboards and graphics based on data source keywords.

  • If necessary, zoom in on the nanosecond timestamp. For retrospective analysis, rewind and playback data streams.

  • Without creating any Java, Scala, or KSQL code, you can unlock Kafka's powers.


You can request a quote on their website.

15. Vertex AI

Vertex AI is Google's platform, similar to Amazon EMR and Azure ML, and is a cloud-based service. This platform has one of the most comprehensive machine learning stacks. The Google AI Platform provides a number of datasets, machine learning libraries, and other tools that users can use to perform data mining and other data science tasks in the cloud.

Key Features:

  • Build with Google's ground-breaking machine learning tools, which were built by Google Research.

  • With 80 percent fewer lines of code required for custom modelling, you can deploy more models, quicker.

  • You can efficiently manage your data and models with MLOps tools, and repeat at scale.


You can request a quote on their website.

16. Orange

Orange is a Python scripting-based open-source data analysis and visualisation tool for data mining. It's a one-stop-shop that includes components for all in-built machine learning algorithms, data pre-processing, a test and score feature for evaluating an algorithm's accuracy on different datasets, and data presentation using graphs.

It includes widgets for nearly all prominent machine learning methods, as well as text mining and bioinformatics add-ons, subset selection, pre-processing, and predictive modeling.

Key Features:

  • For quick qualitative analysis with clean visualisations, use interactive data exploration. While intelligent defaults make fast prototyping of a data analysis process relatively straightforward, the graphical user interface lets you focus on exploratory data analysis rather than scripting.

  • Orange is used in schools, universities, and professional training courses all throughout the world to provide hands-on instruction and visual demonstrations of data science principles.

  • To mine data from other data sources, execute natural language processing and text mining, conduct network analysis, infer frequent itemset, and do association rules mining, use the numerous add-ons available within Orange.


This is a free tool.

17. SAS Enterprise Miner

SAS Enterprise Miner will enable you to cut your data miners' and statisticians' model creation time in half. A self-documenting, interactive process flow diagram environment efficiently maps the complete data mining process to get the best outcomes. It also includes more predictive modeling approaches than any other commercial data mining solution.

Key Features:

  • They are guided through a workflow of data mining tasks by an easy-to-use GUI. The findings of the analytics are presented in simple charts that provide the information needed to make better decisions.

  • Improve the performance of your models by employing cutting-edge algorithms and industry-specific techniques. Visual assessment and validation metrics can be used to verify findings.

  • By displaying forecasts and assessment statistics from models created using different methodologies side by side, you can easily compare them.

  • Scoring code is generated automatically at all phases of model development, avoiding potentially costly errors caused by human rewriting and conversion.


You can request a quote on their website.


KNIME stands for Konstanz Information Miner. The software was first launched in 2006 and is based on an open-source ideology. It has been widely regarded as a leading software for data science and machine learning platforms in recent years, with applications in a variety of industries including banks, medical sciences, publishers, and consulting businesses.

It also has connections for both on-premise and cloud environments, making data transfer easier. Even though KNIME is written in Java, it has nodes that allow users to execute it in Ruby, Python, or R.

Key Features:

  • All of your data may be accessed, merged, and transformed.

  • Use the tools you've chosen to make sense of your data.

  • Support data science practises across the organisation.

  • Make the most of the information you've gathered from your data.


Packages start at $14 per year.

19. RapidMiner

RapidMiner is a go-to tool for data mining, with an exponentially rising user community of over 200,000 users, support for more than 1500 operators and over 400 analytic functions for all data analysis and transformation activities, and access to more than 40 distinct files types.

RapidMiner is a ready-to-use, open-source data mining platform with advanced predictive analytic features such as text analytics, business analytics, machine learning, data mining, and data visualization that requires no scripting. RapidMiner can help with all aspects of the data mining process, including validation, optimization, and display of the results.

Key Features:

  • For constructing machine learning models, everything is seamlessly integrated and optimized.

  • Using a visual workflow designer or automated modeling, create models.

  • Models can be deployed and managed, and they can be turned into prescriptive actions.


You can request a quote on their website.

20. Teradata

Teradata integrates analytics, data lakes, and data warehouses with a powerful, modern analytics cloud architecture. Teradata Vantage can dynamically scale to meet the challenge of expansion for those already in the cloud, and pay-as-you-go enables maximum flexibility. Teradata moves from cloud analytics to answers, whether a business is transitioning to the cloud, has a hybrid infrastructure, or is already fully committed.

Key Features:

  • In a query, Teradata Optimizer can manage more than 60 joins.

  • It's simple to set up, maintain, and manage.

  • You can interact with data in tables using SQL. It serves as an extension.


You can request a quote on their website.

21. MonkeyLearn

MonkeyLearn is a text mining-specific machine learning framework. It has a user-friendly interface and can smoothly be integrated with your existing tools to perform real-time data mining.

You can also connect your studied data to MonkeyLearn Studio, a customizable data visualization dashboard that makes detecting trends and patterns in your data even easier using MonkeyLearn.

Key Features:

  • Every ticket's categorization, routing, and prioritizing can be automated. From your conversations, you can learn about new trends and gain new insights.

  • You can upload CSV/Excel files or use direct integrations, Zapier, or API to connect with your apps.

  • To automatically tag your text, use text analysis models. Choose from predefined models or use machine learning to create your own unique classifiers and extractors.

  • Use your MonkeyLearn tags to create new information about your company and to create new app workflows.


Packages start at $299 per month.

22. Apache Hadoop

Apache Hadoop is basically a software library in the form of a framework that uses basic programming principles to enable the distributed processing of massive data volumes across multiple machines. It's built to expand from a single server to thousands of devices, each with its own computation and storage capabilities.

Rather than relying on hardware to provide high availability, the library is designed to identify and handle problems at the application layer, allowing a highly available service to be delivered on top of a cluster of computers that may all fail.

Key Features:

  • Despite the fact that Hadoop is designed in Java, Hadoop Streaming may be used with any programming language.

  • Hadoop MapReduce is both a programming model and a Hadoop implementation. It has become a popular method for performing complicated data mining on Big Data.

  • It enables users to map and reduce functions commonly used in functional programming.

  • This program is capable of doing huge join operations on massive datasets.

  • User activity analysis, unstructured data processing, log analysis, text mining, and more Hadoop applications are available.


This is a free tool.

23. Weka

Weka is an open-source machine learning software that includes a large number of data mining methods. It was written in JavaScript and produced by the University of Waikato in New Zealand. Weka was created with the intention of analyzing data in the agricultural industry. It is now mostly utilized by researchers and industrial scientists, as well as for education. It's free to download and use under the terms of the GNU General Public License.

Key Features:

  • It has a graphical interface that makes it simple to use and supports many data mining tasks such as preprocessing, classification, regression, clustering, and visualization.

  • Weka comes with built-in machine learning algorithms that allow you to easily test and deploy models without having to write any code.

  • Deep learning is supported by Weka.


This is a free tool.

24. Qlik

Qlik is a platform that uses a scalable and flexible method to address analytics and data mining. It includes a simple drag-and-drop interface that responds quickly to changes and interactions.

Qlik also supports a variety of data sources as well as seamless connections with a variety of application formats via connectors and extensions, a built-in app, or a set of APIs. Using a centralized hub, it's also a wonderful tool for disseminating pertinent analysis.

Key Features:

  • With an end-to-end analytics data pipeline supplying real-time, up-to-date information tailored to trigger rapid actions when they matter most – in the business instant – Active Intelligence provides a state of continuous intelligence.

  • With the only end-to-end, real-time analytics data pipeline, Qlik puts data and analytics together seamlessly. In real-time, liberate your data from silos. Allow users to quickly identify and enrich relevant data, as well as develop derivative data.

  • Qlik turns raw data into reliable, actionable data that's easy to locate, current, and instantly available to Qlik Sense®, Tableau, PowerBI, and other analytics tools - on any cloud you choose.


You can request a quote on their website.

25. Board

Board is a toolkit for management intelligence. Business intelligence and corporate performance management functions are combined in this software. It's intended to provide both business intelligence and business analytics in one package.

Key Features:

  • Allows you to use a single platform to analyse, simulate, plan, and predict.

  • To create unique analytical and planning software.

  • Board Business Intelligence, Corporate Performance Management, and Business Analytics are all combined in All-In-One.

  • It enables companies to create and maintain complex analytical and planning systems.

  • The unique platform aids reporting by allowing users to access multiple data sources.


You can request a quote on their website.

26. Apache Mahout

Apache Mahout is an open-source framework for building scalable machine learning applications. Its purpose is to assist data scientists and researchers in developing and implementing their own algorithms.

This framework, which is written in JavaScript and built on top of Apache Hadoop, focuses on three primary areas: recommender engines, clustering, and classification. It's ideal for large-scale, sophisticated data mining operations involving massive amounts of data. In fact, some of the world's most well-known websites, such as LinkedIn and Yahoo, employ it.

Key Features:

  • Scala DSL with Mathematical Expression.

  • Multiple Distributed Backends are supported (including Apache Spark).

  • Native Solvers for CPU/GPU/CUDA Acceleration Modular Native Solvers for CPU/GPU/CUDA Acceleration Modular Native Solvers for CPU/GPU/


This is a free tool.

27. Scikit-Learn

Scikit-Learn is a free Python software package for machine learning that provides excellent data mining and data analysis capabilities. Classification, regression, clustering, preprocessing, model selection, and dimension reduction are just a few of the characteristics available.

Key Features:

  • Identifying the category to which an object belongs.

  • Predicting the value of an object's continuous-valued attribute.

  • Similar things are automatically grouped into sets.

  • Keeping the number of random variables under consideration to a minimum.

  • Parameters and models are compared, validated, and chosen.

  • Normalization and feature extraction.


This is a free tool.

28. Sisense

Sisense is a data mining tool that you can consider using. It is regarded as one of the best data mining tools. Sisense works very quickly in analyzing and visualizing datasets, regardless of size.

Key Features:

  • You can create dashboards with many visualizations.

  • The tool identifies metrics using calculations and filters.

  • It can handle large data sets.

  • The tool offers a drag-and-drop interface.

  • It can categorize data into one place.


You can request a quote on their website.

Things to Consider When Choosing a Data Mining Tool


Each tool will have a unique user interface that will make it easier for you to interact with the work environment and engage with the data. Some tools are more educational in nature, focusing on offering a general understanding of analytical procedures. Others are tailored to corporate needs, leading users through the process of resolving a specific issue.

Management of Data

Different models for integrating new data may be offered via tools, including data format and size limits. Some tools are better for huge datasets, while others are better for smaller ones. When weighing your alternatives, think about the types of data you'll be working with the most. If your data is now stored in a variety of systems or formats, your best bet is to locate a solution that can accommodate the differences.

Programming Language

The majority of open-source programs (but not all) are developed in Java, but many also support R and Python scripts. It's crucial to consider which languages your programmers are most comfortable with, as well as if they'll be working on data analysis projects alongside non-coders.


We have tried to give you a lot of information about each tool in this post. Ultimately, You should make sure that whichever tool you choose can handle your data and, in the end, give results for your targeted application.


What Is Data Mining?

Statistics, artificial intelligence, and machine learning are all used in data mining. This procedure collects information from data using intelligent approaches, making it comprehensive and interpretable. Data mining enables the discovery of patterns and linkages within data sets, as well as the prediction of trends and behaviors.

Technological improvements have made automated data analysis faster and easier. The greater the size and complexity of the data sets, the more likely it is that meaningful insights will be discovered. Organizations can make appropriate use of valuable information by discovering and comprehending important data in order to make decisions and achieve the stated goals.

What Are Data Mining Tools?

The goal of data mining techniques is to find patterns, trends, and groups in massive volumes of data and to translate that data into more detailed information.

It's a framework that enables you to conduct various forms of data mining analysis.

They can run algorithms on your data sets, such as clustering or classification, and visualize the findings. It's a framework that helps us gain a deeper understanding of our data and the phenomena it represents. A data mining tool is a framework like this.

When Should You Consider Using Data Mining Tools?

Data mining can be used to solve practically any data-related business challenge, including:

  • Increasing profits.

  • Understanding the different types of customers and their preferences.

  • Obtaining new clients.

  • Cross-selling and up-selling should be improved.

  • Retaining and increasing client loyalty.

  • Increasing the return on investment (ROI) from marketing campaigns.

  • Detecting and preventing fraud.

  • Identifying credit risks is a difficult task.

  • Operational performance is being monitored.

What Is the Future of Data Mining?

The future of data mining and data science is bright, as the amount of data continues to grow. By 2022, the total amount of data in our digital cosmos will have grown from 4.4 zettabytes to 44 zettabytes. We'll also generate 1.7 gigabytes of new data for every human on the planet every second.

Mining techniques have changed and improved as technology has advanced, and so have tools for extracting valuable insights from data. Only huge corporations could utilize their supercomputers to examine data once upon a time because the cost of storing and computing data was simply too high. Companies are now using cloud-based data lakes to accomplish all sorts of amazing things with machine learning, artificial intelligence, and deep learning.

What Are the Advantages of Data Mining?

At unprecedented speeds and volumes, data is flooding into businesses in a variety of formats. Being a data-driven company is no longer a choice; the success of your company is on your ability to swiftly uncover insights from big data and incorporate them into business decisions and processes, resulting in superior actions across your organization. With so much data to manage, this may appear to be an impossible undertaking.

By knowing the past and present, and generating accurate predictions about what is likely to happen next, data mining enables businesses to maximize the future.

For example, based on previous customer profiles, data mining can tell you which prospects are most likely to become lucrative customers and which are most likely to respond to a given offer. With this information, you can maximise your return on investment (ROI) by limiting your offer to only those prospects who are most likely to respond and become valuable clients.

Top 28 Data Mining Tools

The world's biggest online directory of resources and tools for startups and the most upvoted product on ProductHunt History.