We performed a comparison between KNIME and Weka based on real PeerSpot user reviews.
Find out in this report how the two Data Mining solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."From a user-friendliness perspective, it's a great tool."
"It's a coding-less opportunity to use AI. This is the major value for me."
"The ETL which helps me to collect, reformat, and load the data from multiple sources into one destination, a storage database."
"KNIME is easy to learn."
"We have found KNIME valuable when it comes to its visualization."
"This solution is easy to use and especially good at data preparation and wrapping."
"The solution allows for sharing model designs and model operations with other data analysts."
"The most valuable feature is the data wrangling, which is what I mainly use it for."
"It is a stable product."
"The path of machine learning in classification and clustering is useful. The GUI can get you results. No programming is needed. No need to write down your script first or send to your model or input your data."
"I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."
"Weka eliminates the need for coding, allowing you to easily set parameters and complete the majority of the machine learning task with just a few clicks."
"Working with complicated algorithms in huge datasets is really easy in Weka."
"With clustering, if it's a yes, it's a yes, if it's a no, it's a no. It gives you a 100% level of accuracy of a model that has been trained, and that is in most cases, usually misleading. Classification is highly valuable when done as opposed to clustering."
"The interface is very good, and the algorithms are the very best."
"It doesn’t cost anything to use the product."
"System resource usage. Knime will occupy total system RAM size and other applications will hang."
"The documentation is lacking and it could be better."
"It's difficult to provide input on the improvement area because it's more of self-learning. However, there are times when I am not able to do certain things. I don't know if it's because the solution doesn't allow me or if it's because of the lack of knowledge."
"If they had a more structured training model it would be very helpful."
"The solution is inconvenient when it comes to wrangling data that includes multiple steps or features because each step or feature requires its own icon."
"KNIME's documentation is not strong."
"They could add more detailed examples of the functionality of every node, how it works and how we can use it, to make things easier at the beginning."
"KNIME could improve when it comes to large data markets."
"The product is good, but I would like it to work with big data. I know it has a Spark integration they could use to do analysis in clusters, but it's not so clear how to use it."
"I believe is there are a few newer algorithms that are not present in the Weka libraries. Whereas, for example, if I want to have a solution that involves deep learning, so I don't think that Weka has that capability. So in that case I have to use Python for ... predict any algorithms based on deep learning."
"Weka could be more stable."
"Not particularly user friendly."
"If you have one missing value in your dataset and this missing value belongs to a specific attribute and the attribute is a numeric attribute and there is only one missing data, whenever you import this data, the problem is that Weka cannot understand that this is a numeric field. It converts everything into a string, and there is no way to convert the string into numerical math. It's really very complicated."
"Within the basic Weka tool, I don't see many tools that are available where we can analyze and visualize the data that well."
"If there are a lot more lines of code, then we should use another language."
"The filter section lacks some specific transformation tools. If you want to change a variable from a numeric variable to a categorical variable, you don't have a feature that can enable you to change a variable from a numeric variable to a categorical variable."
KNIME is ranked 1st in Data Mining with 50 reviews while Weka is ranked 2nd in Data Mining with 14 reviews. KNIME is rated 8.2, while Weka is rated 7.6. The top reviewer of KNIME writes "A low-code platform that reduces data mining time by linking script". On the other hand, the top reviewer of Weka writes "Open source, good for basic data mining use cases except for the visualization results". KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Dataiku Data Science Studio and IBM SPSS Modeler, whereas Weka is most compared with IBM SPSS Statistics, IBM SPSS Modeler, Oracle Advanced Analytics, SAS Analytics and Splunk User Behavior Analytics. See our KNIME vs. Weka report.
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