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."I've never had any problems with stability."
"It is very fast to develop solutions."
"What I like most about KNIME is that it's user-friendly. It's a low-code, no-code tool, so students don't need coding knowledge. You can make use of different kinds of nodes. KNIME even has a good description of each node."
"It has allowed us to easily implement advanced analytics into various processes."
"It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop."
"The product is very easy to understand even for non-analytical stakeholders. Sometimes we provide them with KNIME workflows and teach them how to run it on their own machine."
"The product is open-source and therefore free to use."
"It's very convenient to write your own algorithms in KNIME. You can write it in Java script or Python transcript."
"I like the machine algorithm for clustering systems. Weka has larger capabilities. There are multiple algorithms that can be used for clustering. It depends upon the user requirements. For clustering, I've used DBSCAN, whereas for supervised learning, I've used AVM and RFT."
"Working with complicated algorithms in huge datasets is really easy in Weka."
"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."
"The interface is very good, and the algorithms are the very best."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"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."
"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."
"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."
"They should look at other vendors like Alteryx that are more user friendly and modern."
"When deploying models on a regular system, it works fine. However, when accuracy is a priority, hyperparameter tuning is necessary. Currently, KNIME doesn't have the best tools for this which they could improve in this area."
"The documentation needs a proper rework. "
"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 can improve by adding more automation tools in the query, similar to UiPath or Blue Prism. It would make the data collection and cleanup duties more versatile."
"The main issue with KNIME is that it sometimes uses too much CPU and RAM when working with large amounts of data."
"System resource usage. Knime will occupy total system RAM size and other applications will hang."
"The data visualization part is the area most in need of improvement."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."
"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."
"The visualization of Weka is subpar and could improve. Machine learning and visualization do not work well together. For example, we want to know how we can we delete empty cells or how can we fill in the empty cells without cleaning the data system and putting it together."
"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."
"Weka is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science."
"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."
"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 and Microsoft Azure Machine Learning Studio, 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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