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… more»
How has it helped my organization?
I have used Weka both in teaching and in industry projects, for several types of Data Mining tasks.
What needs 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… more»
Which solution did I use previously and why did I switch?
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.
What other advice do I have?
Data Mining know how is needed to use the solution, but that is what is expected, since this tool is for Data Scientists.
Which other solutions did I evaluate?
Yes, but long ago. I evaluated Oracle Data Mining, Clementine, and SAS Enterprise Miner.
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
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