10 Best Data Mining Tools to Use in 2020

By | May 10, 2020
Data Mining Tools

Data mining provides a primary objective of identifying patterns in the high quantities of data and converting that data into more actionable information. This method uses specific algorithms, artificial intelligence & database systems, and statistical analysis. It intends to obtain information from massive data sets for converting it into an understandable structure for future use. There are various data mining tools available in the modern era that can help any organization or individual to gain the best outcome possible. Therefore let’s discuss the top 10 data mining tools in brief.

Best Data Mining Tools

1. Apache Spark

Apache SparkApache was developed in 2009 by the University of California, Berkeley’s AMPLab. It is a fast large-scale data processing engine that is used to execute applications in Hadoop clusters 100 times faster in memory and 10 times faster on disk. Spark is built on data science, and its concept makes data science smooth. Users can develop logical data that results in multiple tasks through this data mining tool.

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Features of Apache Spark

  • Apache integrates with Hadoop data
  • Works with libraries like GraphX, Spark Streaming and MLib.

Pros

  • Amazing Speed
  • Ease of Use
  • Advanced Analytics
  • Apache Spark is powerful

Cons

  • It doesn’t have an automatic optimization process
  • Fewer Algorithms
  • Small Files Issue
  • Window Criteria

2. Orange

OrangeThis data mining tool is open-sourced, which is fantastic for machine learning and data mining. It supports data visualization as well as it is a component-based tool. This tool provides a more interactive interface to improve the dull process. Orange quickly changes the data into the wanted pattern and provides the best information from it. This mining tool allows the user to make the best decision possible in a short amount of time.

Features of Orange

  • Amazing Analytics/Reporting feature
  • Custom Dashboards
  • Filtered Views
  • Simulation Models
  • Visual Discovery

Pros

  • Easy to use
  • Dashboard is interactive
  • Beneficial for small business
  • Automated analytical facility

Cons

  • Performance issues sometimes
  • The manual process is a bit tricky

3. R Programming

R Programming LanguageThis data mining tool is one of the most excellent data analytics tools because it is essential for a data scientist for data modeling and statistics. It can merely mould the data for the best presentable way possible. R runs and compiles on a vast range of platforms like Windows, macOS, and viz-UNIX. R also provides convenience to automatically install the new packages as per the requirement that can also well combine with big data.

Features of R Programming

  • Comprehensive Language
  • Provides a broad Pattern of Packages
  • Compatibility of Cross-Platform
  • Beneficial for various industries
  • Performs quick calculations

Pros

  • Open Source
  • A considerable amount of packages
  • Highly Compatible
  • Quality Graphing and Plotting
  • Platform Independent

Cons

  • Weak Origin
  • Data Handling
  • Basic Security
  • Complicated Language
  • Lesser Speed

4. KNIME

KNIMEThis data mining tool was developed in January of 2004 by the software engineers’ team at the University of Konstanz. It is an open-source, integrated analytics and reporting tool. It allows the user to analyze the data model through visual programming, and it also integrates various components for machine learning and data mining via its concept of modular data-pipelining.

Features of KNIME

  • Big Data Extensions
  • Data Blending
  • Tool Blending
  • Meta Node Linking
  • Local Automation
  • Data Manipulation

Pros

  • Handles large quantities of data
  • Easily integrates with cloud and data environments.

Cons

  • Some features are not easy to use.
  • It can drain memory.

5. Sisense

SISENSESisense offers immediate insights for the user anywhere in the organization. This data mining tool enables a user to generate visual dashboards as well as reports to state every data, reveal underlying patterns, and make data-driven decisions. It allows mixing data from different references to develop a shared repository and further improves data to produce sharp reports that get shared over departments for reporting.

Features of Sisense

  • No data preparation required
  • Instant development
  • Minimum total cost
  • Users can simply integrate this tool from other products.

Pros

  • It has a friendly user interface.
  • Excellent support
  • Easy upgrades
  • Integrates amazingly with various data sources.
  • This tool is flexible and provides easy customization.

Cons

  • Hard to manage and improve analytic cubes.
  • A limited type of visualizations.

6. SSDT (SQL Server Data Tools)

SQL ServerThis data mining tool is universal and licensed software that extends all aspects of database improvement in a Visual Studio IDE. BIDS is the past environment produced by Microsoft for data analysis and offers business intelligence solutions. Users can use SSDT transact- a design capacity of SQL for building, maintaining debugging, and refactor the database. You can directly work with the databases or with the connected database for providing on or off-premise facility

Features of SSDT

  • Various supported editions
  • Online product documentation
  • Career opportunities
  • Free developer version
  • Growing online community
  • Lots of online documentation.

Pros

  • Microsoft Premier Support
  • It has a cloud database support
  • Lots of tools and applications
  • Support in Linux

Cons

  • Expensive enterprise version
  • Difficult licensing process
  • Complex performance
  • No primary support for source control

7. SAS Data mining

SASThis data mining tool consists of a statistical analysis system, and it was created for data management and analytics. SAS provides graphical UI for new users so that they can use it without any issue. SAS introduced a vast number of outputs for various SAS modules and consumer intelligence. These products are introduced for social media, marketing analytics, and web, which is used for views and profiling consumers.

Features of SAS Data mining

  • This data mining tools assist you in analyzing big data
  • It is an excellent tool for data mining, text mining, and optimization.
  • SAS allows shared memory processing structure which is extremely scalable

Pros

  • Easy to learn
  • Easy to debug
  • Tested algorithms
  • Data Security
  • Nice Output

Cons

  • High cost
  • It is not open source
  • Difficult Text Mining
  • Difficult than R

8. BOARD

Data Mining ToolsThis mining tool is management intelligence software that combines the features of business intelligence as well as corporate performance management. It is created to provide business analytics and business intelligence in a particular package. The exclusive platform serves to report by obtaining multiple data sources.

Features of BOARD

  • It allows a user to Analyze, plan, predict, and simulate handling a single platform.
  • It helps to create customized analytical as well as planning applications.
  • Board All-In-One connects BI, Business Analytics, and Corporate Performance Management.
  • It allows any to dev business for developing and maintaining complex analytical.

Pros

  • Amazing functionality
  • Good customer support
  • Predictive analysis is appropriate for the organization.
  • Flexible and value for money.

Cons

  • Issues regarding access support system
  • A bit hard to use for the beginners.

9. Teradata

teradataThis data modeling tool is a vast parallel open-sourced processing system that helps a user to develop large-scale data warehousing applications. It is supported in various platforms like Windows, Linux, and UNIX, and it also provides load & unloads utilities to move data from the Teradata System.

Features of Teradata

  • Teradata Optimizer helps a user to 64 joins in a query.
  • The low total cost of ownership.
  • It supports SQL for interacting with any data which is saved in tables.
  • It assists a user in sharing the data to the disks automatically with no standard intervention.

Pros

  • The connectivity portion is fantastic.
  • Stable program
  • Massive Parallel Processing.
  • data protection functionality

Cons

  • The UI of this tool can be improved as a user-friendly model.
  • Improved functionality
  • Data migration from Teradata to another RDBMS is difficult.
  • Only usable for a vast number of data warehousing.

10. Dundas

Dundas BIThis data mining tool is the best data mining tool that is used to build and view interactive, reports, and dashboards. Users can use Dundas BI in the form of a primary data portal for the organization. This tool is beneficial for advanced analytics and data analytics.

Features of Dundas

  • Server purpose with complete product functionality
  • Access all manner of data experts
  • Customizable data visualizations
  • Quick drag and drop feature
  • You can visualize the data easily.

Pros

  • It provides customer support.
  • User-friendly UI system
  • Quick and easy updates

Cons

  • Need improvement in features
  • It requires a sliding calendar and range filter.

Conclusion

In this article, we have discussed some factors regarding the importance of the best tools. Every tool has some key features, pros, and cons so that you can differentiate between various tools and find the best one for you. This is not the end of technology, so we will try to update you regarding the best data mining tools of the generation.

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