Data mining is the process of finding patterns and relationships in raw data.
Companies need to glean insights from data so they can make decisions and understand customer needs. But with large volumes of data streaming in from different sources ‒ in most cases, unstructured ‒ finding what’s relevant can be a real challenge.
Analyzing data manually is costly, time-consuming, and tedious for those who have to go through data in finite detail. Plus, manual analysis is ineffective as your business grows (along within your data).
Using data mining tools, businesses can automate manual data analysis and make sense of large amounts of data to uncover insights and solve problems.
Looking for the right data mining tool for your business? Read on to learn about the best data mining tools on the market.
Let’s jump right into our list of the 10 best data mining tools:
MonkeyLearn is a machine learning platform with a full suite of text mining tools. Available in a user-friendly interface, you can easily integrate MonkeyLearn with your existing tools to perform data mining in real-time.
Start immediately with pre-trained models, or build a customized solution to cater to more specific business needs.
MonkeyLearn supports various text mining tasks, from finding topics, sentiment, or intent in your data (classification models), to identifying relevant keywords or named entities (extraction models). This sentiment analyzer, for example, can understand emotions in text and classify opinions as Positive, Negative, and Neutral:
But that’s not all: with MonkeyLearn Studio, you can combine your analysis with data visualization, and create customizable dashboards that make it easier to detect trends and patterns, and get even more granular insights from your data.
Take a look at MonkeyLearn’s plans and pricing.
RapidMiner is an open-source data science platform for building predictive analytics models. It provides tools for the different stages of data mining, such as preparing and cleaning data, validating a model, and visualizing an outcome.
With its drag-and-drop visual interface and over 1,000 machine learning algorithms and pre-built templates for specific use cases, like fraud detection and customer churn, RapidMiner Studio enables non-programmers to create predictive workflows in a simple way. Programmers can extend RapidMiner capabilities by adding R and Python extensions, which allow them to seamlessly run code or scripts within RapidMiner.
Once you’ve created your workflows, you can visualize your data inside RapidMiner Studio using customizable charts and graphs. This can help you spot patterns, outliers, and trends in your data.
Last but not least, this platform has a large and enthusiastic community of users, who are always on hand to help.
There’s a free plan available that allows you to analyze up to 10,000 data rows.
Oracle Data Mining is a component of Oracle Advanced Analytics that enables data analysts to build and implement predictive models. It contains several data mining algorithms for tasks like classification, regression, anomaly detection, prediction, and more.
With Oracle Data Mining, you can build models that help you predict customer behavior, segment customer profiles, detect fraud, and identify the best prospects to target. Developers can use a Java API to integrate these models into business intelligence applications to help them discover new trends and patterns.
Oracle offers a 30-day free trial.
IBM SPSS Modeler is a data mining solution, which allows data scientists to speed up and visualize the data mining process. Even users with little or no programming experience can use advanced algorithms to build predictive models in a drag-and-drop interface.
With IBM’s SPSS Modeler, data science teams can import vast amounts of data from multiple sources and rearrange it to uncover trends and patterns. The standard version of this tool works with numerical data from spreadsheets and relational databases. To add text analytics capabilities, you need to install the premium version.
A 30-day free trial is available.
It supports different data mining tasks, like preprocessing, classification, regression, clustering, and visualization, in a graphical interface that makes it easy to use. For each of these tasks, Weka provides built-in machine learning algorithms which allow you to quickly test your ideas and deploy models without writing any code. To take full advantage of this, you need to have a sound knowledge of the different algorithms available so you can choose the right one for your particular use case.
Weka was originally designed to analyze data in the field of agriculture. Now, it is mainly used by researchers and industrial scientists, as well as for educational purposes. It is available to download for free under a GNU General Public License.
KNIME is a free, open-source platform for data mining and machine learning. Its intuitive interface allows you to create end-to-end data science workflows, from modeling to production. And different pre-built components enable fast modeling without entering a single line of code.
A set of powerful extensions and integrations make KNIME a versatile and scalable platform to process complex types of data and use advanced algorithms.
With KNIME, data scientists can create applications and services for analytics or business intelligence. In the financial industry, for instance, common use cases include credit scoring, fraud detection, and credit risk assessment.
H2O is an open-source machine learning platform, which aims to make AI technology accessible to everyone. It supports the most common ML algorithms and offers Auto ML functions to help users build and deploy machine learning models in a fast and simple way, even if they are not experts.
H2O can be integrated through an API, available in all major programming languages, and uses distributed in-memory computing, which makes it ideal when analyzing huge datasets.
Orange is a free, open-source data science toolbox for developing, testing, and visualizing data mining workflows.
It is a component-based software, with a large collection of pre-built machine learning algorithms and text mining add-ons. It also has extended functionalities for bioinformaticians and molecular biologists.
Orange also allows for interactive data visualization, offering numerous graphics like silhouette plots and sieve diagrams, and non-programmers can perform data mining tasks through visual programming in a drag-and-drop interface. Developers, meanwhile, can opt to use Python scripting.
Apache Mahout is an open-source platform for creating scalable applications with machine learning. Its goal is to help data scientists or researchers implement their own algorithms.
Apache Mahout is free to use under the Apache licence and it’s supported by a large community of users.
SAS Enterprise Miner is an analytics and data management platform. Its goal is to simplify the data mining process to help analytics professionals turn large volumes of data into insights.
Through an interactive graphical user interface (GUI), users can generate data mining models fast, and use them to solve critical business issues. SAS provides a rich set of algorithms for preparing and exploring data, and for building advanced predictive and descriptive models.
Companies can use SAS Enterprise Mining for fraud detection, resource planning, and increase response rates on marketing campaigns, among other applications.
A free software trial and customized pricing packages are available.
Data mining tools can help your business make better decisions, by revealing hidden relationships and patterns in data.
There are many options available: knowing which tool is best for you will depend on your goals and the type of data that you want to analyze.
Want to build your own data mining solution? Get familiar with some of the open-source tools we mentioned above.
Looking for an all-in-one text analysis and data visualization platform that’s easy to use? Look no further than MonkeyLearn Studio.
December 22nd, 2020