What are data mining tools? (With 15 software examples)

Updated 7 June 2023

Due to the development of advanced data analysis tools, businesses are becoming more data-driven than ever. Data mining is one tool that helps businesses learn more about customers' needs, make better-informed decisions, and increase revenues. Understanding what data mining is and what tools are available can help you find solutions to your business needs. In this article, we answer the question 'What are data mining tools? and provide examples of currently available data mining software.

What is data mining?

Data mining is a specialist area of business analytics that combines data analysis, computer science, and statistics skills. The process involves analysing large data sets to identify patterns and anomalies that can be useful to businesses. With effective data mining processes, companies can turn raw data into analyses that they can employ to inform strategic decisions. To get a thorough analysis of data sets, the data mining process typically involves:

  • applying specific algorithms

  • performing statistical analysis

  • utilising artificial intelligence

The term data mining could seem to imply that the process involves extracting data itself. In reality, the goal is to extract patterns and knowledge from data. The patterns identified through the data mining process can help to summarise the overall findings from the data set. This process can reveal niche information, like how long site visitors spend on a particular product page. What you find depends on your purpose and the business' focus. Some insights data mining can provide include:

  • learning more about customer behaviours to increase sales

  • making informed decisions to improve operational efficiency

  • understanding how to re-target customers or prospective customers

Related: Top data analyst interview questions and example answers

What are data mining tools?

The answer to 'What are data mining tools?' is essentially software programs that help execute data mining techniques, create data models and test those models. They're frameworks that allow companies to perform different types of data mining analysis. By using data mining tools, businesses can gain greater insight into the findings within their data sets.

Related: What is software consulting? (A comprehensive guide)

Please note that none of the companies mentioned in this article are affiliated with Indeed.

Examples of data mining tools

There are numerous data mining tools on the market, and the software that companies choose depends on their unique requirements. Different programs offer varying levels of sophistication. Depending on how businesses want to explore their data sets, there is a choice of options. Some of the most common data mining tools include:

1. Apache Mahout

Apache Mahout is an open-source platform that allows users to create scalable machine learning algorithms. The software operates over Apache Hadoop, which is an open-source framework that allows the storing and processing of big data sets. Some of the data mining techniques that Apache Mahout focuses on include:

  • classification

  • clustering

  • recommendation engines

This data mining tool is best suited for complex, large-scale data mining projects that involve huge volumes of data. Apache Mahout is free to use under the Apache licence, and the software receives support from a wide community of users, including leading web companies. Some of the well-known companies that use Apache Mahout include Adobe, Facebook, Foursquare, and Yahoo.

2. DataMelt

DataMelt, also known as DMelt, offers an interactive structure for data analysis and visualisation. It's a computation and visualisation environment primarily designed for engineers, scientists and students. Typically, you can use this for data mining, statistical analysis and analysis of large data sets. The industries that most commonly use it are natural sciences, engineering and financial markets because of its use of mathematical libraries, which provide random number generation and algorithm and curve fitting. It also uses scientific libraries, which are necessary for generating 2D and 3D plots.

Related: How to become a data analyst

3. H2O

H2O is another open-source platform. This software focuses on making artificial intelligence technology accessible to all. As a machine learning platform, H2O supports most of the common machine learning algorithms and offers users automatic machine learning functions to help them build and deploy models quickly and simply.

4. IBM SPSS Modeler

IBM SPSS Modeler allows data scientists to visualise and speed up the data mining process. This data mining tool helps users, even those with little to no programming experience, apply advanced algorithms to build predictive models. The drag-and-drop interface of IBM SPSS Modeler speeds up operational tasks so that data scientists can import and rearrange large volumes of data from various sources. Common functions of this software include:

  • data preparation

  • discovery

  • model management

  • predictive analytics

Related: How to become a data scientist in four steps

5. Kaggle

Kaggle initially started as a platform for machine learning competitions but now offers a public, cloud-based data science platform. It's the largest community of data scientists and machine learning professionals. Kaggle offers users access to code, public data sets and public notebooks to use in data science implementations. The large online community of professionals is also a great resource for overcoming specific challenges.

6. Knime

Knime is a free, open-source platform that helps users build, deploy and scale data mining and machine learning techniques. The software aims to make predictive intelligence accessible to everybody, regardless of their experience levels. With an easy-to-use interface, Knime lets users create end-to-end data science workflows that can serve to process large and complex data sets. Knime is commonly employed in the finance industry to:

  • credit score consumers

  • detect fraud

  • perform credit risk assessments

7. Monkey Learn

Monkey Learn is a machine learning platform that specialises in text mining. It is most commonly used to improve processes in customer support and social media departments by:

  • analysing customer sentiment

  • automatically detecting negative feedback on social media channels

  • tagging and routeing customer support tickets

This software provides both pre-trained text mining tools and customizable tools to suit specific business needs. Monkey Learn also has data visualisation tools that allow companies to create customizable dashboards that help identify patterns and trends.

Related: How to become a data manager plus traits and best practices

8. Oracle Data Mining

Oracle is a leader in database software, and its data mining tool enables users to build and implement predictive models. Oracle Data Mining has a graphic user interface that guides users through the process of creating, testing, and implementing data models. It also features several data mining algorithms to perform tasks, such as:

  • anomaly detection

  • classification

  • prediction

  • regression

9. Orange

Orange is a free machine learning and data mining software suite. It offers a broad range of features for developing, testing, and visualising data mining workflows. Orange has an extensive collection of pre-built machine learning algorithms, text-mining add-ons, and even extended functionalities for specialist fields, such as bioinformatics. With a drag-and-drop user interface and interactive data visualisation tools, Orange enables non-programmers to perform data mining tasks.

10. Python

Python is an open-source programming language that's typically quick to learn. While it doesn't have the easy-to-use interfaces and extra support that proprietary software offers, it's freely available for anybody to learn and create their own graphical interfaces. Python has a large library of packages that can help build systems for creating custom-made data models. Many packages, such as H2O, Orange and Rapid Miner, are written in Python's computing language.

Related: 10 of the most in-demand coding languages and their uses

11. Rapid Miner

Rapid Miner is a free, open-source data science platform. It provides a drag-and-drop interface and pre-built models that allow non-programmers to create predictive workflows. The software platform offers an integrated environment for various stages of data modelling, including:

  • data preparation

  • data cleansing

  • exploratory data analysis

  • deep learning

  • data visualisation

  • predictive analysis

While Rapid Miner is a great tool for non-programmers, it also has extensions for experienced programmers to tailor their data mining to business needs.

12. Rattle

Rattle is a free, open-source data mining tool that uses the R programming language. It has extensive data mining features and users can employ Rattle to:

  • build supervised and unsupervised machine learning models

  • compare model performance

  • create visual summaries of data sets

  • get statistical summaries of data

  • transform data for data models

13. SAS Enterprise Miner

SAS Enterprise Miner focuses on simplifying the data mining process to help analytics professionals transform large volumes of data into useful insights. This analytics and data management platform have an interactive graphical user interface that allows users to quickly generate data mining models that serve to solve business issues. Common uses for SAS Enterprise Miner include:

  • fraud detection

  • improving engagement with marketing campaigns

  • resource planning

14. Teradata

Teradata is a cloud data analytics platform that prides itself on its ease of use. Users don't need programming experience to code complex machine learning algorithms with Teradata. The simple interface allows users to implement Teradata's database management system quickly for large-scale projects.

15. Weka

Weka, or Waikato Environment for Knowledge Analysis, is an open-source machine learning software that offers a vast collection of algorithms for data mining. To utilise all of Weka's functionalities, a sound knowledge of the different algorithms helps users choose the most appropriate algorithm for their business needs. Some of the tasks Weka supports include:

  • classification

  • clustering

  • preprocessing

  • regression

  • visualisation

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