Weka is a very easy to use Data Mining solution. It is great for learning and for doing small experiments before exploring the data deeper. Another important feature is the number and diversity of algorithms that make Weka an excellent solution for rapid testing. The several interfaces that are provided also allow a diverse range of applications and uses. Another important aspect is the ability to easily integrate new algorithms to the solution, and also it’s integration in terms of Java code.
Improvements to My Organization
I have used Weka both in teaching and in industry projects, for several types of Data Mining tasks.
Room for Improvement
Scalability and performance are the main aspect of improvement in Weka, since it has the main Java limitations, regarding the JVM. Besides that, the pre-processing part of Weka is the hardest to use aspect of it.
Use of Solution
I've used it for more than 10 years.
No issues with deployment.
No issues with stability.
Yes, fine tuning the JVM memory is something to be careful about.
Customer Service and Technical Support
It is open source and customer service is not something that is given. However, there is a community of Weka users and some documentation that can help with the use of Weka. Technical Support
Same thing as the customer service.
I used the old Clementine solution (now in the IBM portfolio). Weka ends up being more versatile, both in terms of diversity of algorithms, integration flexibility and there are less costs.
The setup is straightforward, just download and start using.
I implemented in-house and for other companies.
High, since it has low costs and is very easy to use.
Other Solutions Considered
Yes, but long ago. I evaluated Oracle Data Mining, Clementine, and SAS Enterprise Miner.
Data Mining know how is needed to use the solution, but that is what is expected, since this tool is for Data Scientists.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Jul 25 2015