Top 15 Data Mining Tools To Discover Patterns And Correlations

What Is Data Mining?

Data Mining is a process of extracting and discovering patterns in large datasets using methods of statistics, machine learning, and database. It is the first step towards converting raw data into meaningful information. The key objective of data mining is to extract information from a data set and prepare a comprehensible structure so that it can be ready for further use.

Popular Data Mining Tools

• Orange
• IBM SPSS Modeler
• Weka
• KNIME
• Rattle
• SAS Data Mining
• SQL Server Data Mining
• Tanagra
• ELKI
• Sisense
• Anaconda
• DataMelt
• Teradata

Oracle Data Mining:

Oracle Data Mining is a part of?Oracle Database Enterprise Edition. It offers many data mining and data analysis algorithms for association, regression, anomaly detection, feature selection, classification, and prediction.

It embeds data mining within the Oracle database. This means, there is no need to extract and transfer data into standalone tools or places, or specialized servers. ODM’s integrated approach helps organizations effectively manage data and discover patterns, trends, and insights from the data. In ODM, data mining tasks can run asynchronously as a part of database processing pipelines.

ODM is widely used within the Oracle database and allows a user to utilize all aspects of Oracle’s technology stack as part of an application. It is very popular and one of the powerful data mining tools offering data mining and data analysis algorithms to discover new insights, identify trends, and predict customer behavior.

RapidMiner:

RapidMiner is a popular data science software platform that offers an integrated environment for data preparation, text mining, predictive analytics, deep learning, and machine learning. It is developed on an open core model and used by data scientists across the world.

It was formerly known as YALE( Yet Another Learning Environment). RapidMiner offers powerful data mining and machine learning capabilities used by world-leading organizations and brands for data science and machine learning.

RapidMiner has got many accolades from the data science community and popular firms such as Forrester, Gartner, G2 Crowd, and so on. It is an end-to-end data science platform offering data preparation, model operations, and machine learning solutions across industry verticals. It has a proven track record of helping organizations better understand data, drive revenue, cut costs, and avoid risks.

Orange:

Orange is an open-source data visualization, machine learning, and data mining toolkit. It offers a set of powerful, rich, and interactive data visualization through easy-to-use interfaces and rich visualization widgets. It is ideal for beginners, students, and academics. With built-in machine learning algorithms, data pre-processing features, add-ons for text mining, predictive modeling, and data visualization, Orange is widely used by data professionals without having to learn any programming language. You will require basic knowledge of data mining and data science concepts and algorithms for a better understanding of data.

There are various widgets and extensions available in Orange through which data analysts can use this tool for a wide range of tasks.

IBM SPSS Modeler:

IBM SPSS Modeler is a data mining and text analytics software application powered by IBM. Its easy-to-use visual interface lets users leverage statistical and data mining algorithms without or with little programming experience. It is used to build predictive models and perform analytics tasks. It eliminates unnecessary complexities in the process of data transformation. It can be used for fraud detection and prevention, customer analytics, risk management, healthcare quality improvement, quality management, and so on.

It is available in two separate editions by IBM – SPSS Modeler Professional and SPSS Modeler Premium.

Weka:

Weka stands for Waikato Environment for Knowledge Analysis.

It was developed by the University of Waikato, New Zealand. This free and easy-to-use data mining software offers a collection of tools and algorithms for data analysis and predictive modeling. It is fully implemented in Java and runs on almost any computing platform. Weka contains a set of data preprocessing and modeling techniques. It is used for data mining tasks such as clustering, regression, classification, and data preprocessing. Ease of use, intuitive GUI, and a vast collection of data mining and machine learning algorithms make it a widely-used data mining tool among data professionals.

It is also used in the data mining and predictive analytics component of the Pentaho Business Intelligence Suite.

KNIME:

Konstanz Information Miner (KNIME) is an open-source data analytics, reporting, and integration platform. It was developed by a team of engineers at the University of Konstanz. It is written in Java and makes use of an extension mechanism to provide additional functionalities through the plugin. It includes modules for data integration, data transformation, data mining, analysis, and analytics. It is a comprehensive platform for data science and machine learning tasks that help organizations take control of their data and perform various analyses on data to detect anomalies, identify risks, and predict trends, and manage customers.

Rattle:

Rattle is a free and open-source software offering a graphical user interface (GUI) for data mining using the R programming language. It is a popular statistical package used in various departments and other organizations around the world for data mining activities.

It provides functionalities for statistical analysis, model generation, machine learning models, exploratory data analysis, and graphic data visualization. All interactions through GUI are captured as an R script and can be executed in R without requiring the Rattle interface.

SAS Data Mining:

SAS stands for Statistical Analysis System.

SAS is the longest-standing leader in the Gartner Magic Quadrant for Data Science and Machine Learning Platforms in its eight-year existence. With easy-to-use and drag-and-drop interfaces, SAS has made it easier for organizations to better understand data, perform advanced analytics, visualize data, and simplify data preparation.

It is a statistical software suite developed by SAS Institute for data mining, modification, data management, analytics, and data visualization.

SAS Enterprise Miner is an advanced analytics and data mining tool designed to help users develop descriptive and predictive models. SAS Enterprise Miner offers is not a free version and is used by a large number of companies across the world to create better and accurate predictive models for the most lucrative opportunities.

It is an ideal tool for data mining, text mining, and optimization

SQL Server Data Mining:

SQL Server is mainly used as a data storage tool in many organizations. With the advancement in data storage and management, SQL server offers several data mining features that can be used for various data mining activities.

SQL Server Data Mining offers nine data mining algorithms for classification, estimation, clustering, forecasting, sequencing, and association. It also includes multiple standard algorithms such as EM and K-means clustering models, logistic regression and linear regression, decision trees, and many more.

SQL Server Data Mining is now available as SQL Server Machine Learning. It lets the user execute Python and R Scripts in a database and also deploy machine learning models within a database to prepare and clean data.

Tanagra:

Tanagra is a free suite of machine learning software for research and academic purpose. It supports several standard data mining tasks such as visualization, regression, factor analysis, classification, clustering, and association rule mining. It is widely used in French-speaking universities and studies. This project is the successor of SIPINA which implements various supervised learning algorithms.

It acts as more as an experimental platform and allows users to add their own data mining methods and compare performances with others. It is a free, powerful data mining tool used by novice developers, students, researchers, and data professionals.

ELKI:

ELKI is an open-source data mining software written in Java. It was developed for use in research and learning. The focus of ELKI is unsupervised methods in cluster analysis and outlier detection. It is used by researchers, data scientists, and students. In this platform, data mining algorithms and management tasks are separated and thus independent evaluation is possible. It is open to arbitrary data types, measures, file formats, or distance. It offers a large collection of highly parameterizable algorithms for an easy and fair evaluation of algorithms.

Sisense:

Sisense is BI and big data analytics software that also offers functionalities for preparing, querying, and visualizing datasets. Sisense’s Insight Miner uses machine learning methods and algorithms to provide users with hidden insights from the data.

Sisense is a leading cloud analytics platform and offers a wide range of features to manage, monitor, and visualize data in order to gain game-changing business insights. Sisense’s products and services are widely used in the data world and it is one of the leading BI and analytics platforms in the market.

Anaconda:

Anaconda is the birthplace of Python data science. It is the world’s most popular data science platform. It is a distribution of the Python and R programming languages for data science, machine learning applications, large-scale data processing, and predictive analytics.

It comes with many of the popular and most useful data science tools. It is an exceptionally large and thriving community of data scientists with a large number of packages and active users. Anaconda offers individual, team, and enterprise editions. It has over 7500 packages available in its cloud-based repository. It is great for deep models and neural networks.

DataMelt:

DataMelt is a software program for scientific computation, data analysis, and data visualization. It is used for curve fitting, data mining, and statistical data analysis. It is written in Java and thus, it can run on any platform that has JVM. It is designed for data engineers, students, and researchers. The DataMelt aims to create a data-analysis environment using open-source packages with UIs and tools. It uses high-level programming languages such as Jython, JRuby, Java, and Apache Groovy.

It is a powerful data analysis software particularly useful for the analysis of large numerical data volume, data mining, and statistical data analysis.

Teradata:

Teradata Warehouse Miner allows users to perform data mining within a Teradata warehouse. It offers logistic regression, Decision Tree, Clustering, Association Rule, and Linear Regression Algorithms.

Teradata is a provider of database and analytics-related software, products, and services. It is named a Leader in The Forrester Wave?: Cloud Data Warehouse, Q1, 2021. It is used by a number of well-known brands and industries for effective data management, analytics, and data warehousing. It is recognized as a leader in data management and analytics.

I have summed up the top data mining tools in this blog post. If you think that I’ve missed out on any well-known data mining tool that should be on this list, you can tell me via comments.

To select the best data mining tool, you need to have closer look at your requirements and other parameters that impact your selection. Before you choose any data mining tool, make sure you select the tool that suits your needs.

Data is a valuable asset and organizations must consider it, mine it, utilize it, and make better decisions that drive revenues and growth.