Data Mining Tools
Data mining can be difficult, especially if you don’t know what some of the best free data mining tools are. At Springboard, we’re all about helping people to learn data science, and that starts with sourcing data with the right tools.
Last year, the data mining experts at Kdnuggets.com conducted regular surveys of thousands of their readers. Here’s their list of the 10 most popular free data mining tools, with reader share percentages and a comparison to the prior year:
List of data mining tools and share (among KDnuggets readers):
|Data Mining Tool||2015 Share||2014 Share|
Notice that the old standby, Excel, took a dive this year. The more powerful Python and Spark made the most significant gains on the list. Spark’s popularity reflects the expansion of Hadoop and big data tools into mainstream business. A few popular that didn’t make the list include H2O (0xdata), Actian, MLlib and Alteryx.
Wondering which of these tools might be most relevant for you? Here’s some more information to help you narrow down the list and identify the best data mining tool to use.
There’s no mystery why R is the superstar of free data mining tools on this list. It’s free, open source and easy to pick up for people with little to no programming experience. Some people have even referred to R as “Excel for a new generation.” There are literally thousands of pre-built packages available for you to download so you can start running the most advanced algorithms against extremely large data sets.
RapidMiner and R are at the top of their games in terms of popularity. RapidMiner tends to be the preferred choice for startups and next gen “smart plant” manufacturers. Mobile apps and chatbots tend to depend on this software platform for machine learning, rapid prototyping, app development, text mining and predictive analytics for customer experience.
If you’re working on large-scale projects like textual analytics, you’ll find the IBM SPSS workbench and its visual interface extremely valuable. It allows you to generate a variety of data mining algorithms with no programming. You would also use this for anomaly detection, Bayesian networks, CARMA, Cox regression and basic neural networks that use multilayer perceptrons with back-propagation learning. Not for the faint of heart.
Turn to this tool for enterprise-level work. It captured leading top-right-corner evaluations by Forrester and Gartner, so the investors will be on board. SAS is also a good choice for predictive market models dimension-reduction techniques and creating interactive visualizations for presentations. You can only access a limited free version of this software through educational institutions. If you do contract work for a large organization that runs SAS Enterprise, take advantage of every moment.
As a free and open source language, Python is most often compared to R for ease of use. Unlike R, Python’s learning curve tends to be so short it’s become legendary. Many users find that they can start building data sets and doing extremely complex affinity analysis in minutes. The most common business-use case-data visualizations are straightforward as long as you are comfortable with basic programming concepts like variables, data types, functions, conditionals and loops.
A great example of what Python can create, Orange is a software suite of machine learning components and data manipulation processes. It’s free and ideal for beginners. The most common visualizations needed for a professional career are just a few clicks away, including text mining, heat maps, dendrograms and scatter plots. Orange even learns your preferences as you use it.
People with database backgrounds are more comfortable with KNIME‘s user-friendly framework. It’s built on the idea of modular data pipe-lining and interactive tables. The name is short for Konstanz Information Miner, referring to the German university where it was born. This tends to be the first choice of those in life sciences, who extol the virtues of its intuitive GUI.
The attraction of Spark is plowing through vast oceans of data center traffic with ease. Spark jobs run by Python are being deployed in data-intensive projects by everyone from NASA to Amazon. If you’re moving into a big data or network edge/IoT career, you’ll probably need to know Spark eventually, one of the best open source data mining tools to deal with massive amounts of data.
If you want to get out on the cutting edge, start learning H2O. In its less than five years, it’s been installed thousands of times, with applications for fraud detection at Paypal and customer metrics for the popular WordPress plugin ShareThis. Like R, it has a very active and enthusiastic user community that’s propelling its growth.
Before you make a final decision, start from the end, and work backward. Are you digging for information to find:
- Actions predictive of customer behavior?
- Ways to improve efficiency or quality in production?
- Recurring patterns in market movements?
- Irregularities that indicate fraud?
- Deeper insights into natural laws?
- Something that’s never been done before?
Apply your data mining skills to help you select the right tool for the job. Ideally, you would try everything in this data mining tools list (and more) in the near future to see which are the best data mining tools for you with your individual approach to comprehending the implications of the data.
Do you think we’ve missed one of your best data mining tools? Let us know in the comments below!