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."It is a stable solution...It is a scalable solution."
"From a user-friendliness perspective, it's a great tool."
"The visual workflow tools for custom and complex tasks always beat raw coding languages with the agility, speed to deliver, and ease of subsequent changes."
"We have found KNIME valuable when it comes to its visualization."
"One of the greatest advantages of KNIME is that it can be used by those without any coding experience. those with no coding background can use it."
"The ETL which helps me to collect, reformat, and load the data from multiple sources into one destination, a storage database."
"It is very fast to develop solutions."
"It can handle an unlimited amount of data, which is the advantage of using Knime."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"It is a stable product."
"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."
"The interface is very good, and the algorithms are the very best."
"Working with complicated algorithms in huge datasets is really easy in Weka."
"Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data."
"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."
"Weka is a very nice tool, it needs very small requirements. If I want to implement something in Python, I need a lot of memory and space but Weka is very lightweight. Anyone can implement any kind of algorithm, and we can show the results immediately to the client using the one-page feature. The client always wants to know the story. They want the result."
"One thing to consider is that the prebuilt nodes may not always be a perfect fit for your specific needs, although most of the time, they work quite well."
"From the point of view of the interface, they can do a little bit better."
"If they had a more structured training model it would be very helpful."
"I would prefer to have more connectivity."
"I've had some problems integrating KNIME with other solutions."
"I'd like something that would make it easier to connect/parse websites, although I will fully admit that I'm not as proficient in KNIME as I would like to be, so it could be I'm just missing something."
"In the last update, KNIME started hiding a lot of the nodes. It doesn't mean hiding, but you need to know what you're looking for. Before that, you had just a tree that you could click, and you could get an overview of what kind of nodes do I have. Right now, it's like you need to know which node you need, and then you can start typing, but it's actually more difficult to find them."
"The most difficult part of the solution revolves around its areas concerning machine learning and deep learning."
"A few people said it became slow after a while."
"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."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."
"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."
"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."
"Not particularly user friendly."
"Weka is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science."
"Within the basic Weka tool, I don't see many tools that are available where we can analyze and visualize the data that well."
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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