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."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."
"It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured."
"KNIME is quite scalable, which is one of the most important features that we found."
"We leverage KNIME flexibility in order to query data from our database and manipulate them for any ad-hoc business case, before presenting results to stakeholders."
"This solution is easy to use and especially good at data preparation and wrapping."
"From a user-friendliness perspective, it's a great tool."
"Since KNIME is a no-code platform, it is easy to work with."
"KNIME is fast and the visualization provides a lot of clarity. It clarifies your thinking because you can see what's going on with your data."
"Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data."
"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."
"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."
"The interface is very good, and the algorithms are the very best."
"It is a stable product."
"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."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"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."
"In my environment, I need to access a lot of servers with different characteristics and access methods. Some of my servers have to be accessed using proxy which is not supported by KNIME, so I still need to create the middleware to supply the source of my KNIME configurations."
"I would like it to have data visualitation capabilities. Today I'm still creating my own data visualtions tools to present my reports."
"System resource usage. Knime will occupy total system RAM size and other applications will hang."
"The ability to handle large amounts of data and performance in processing need to be improved."
"It could input more data acquisitions from other sources and it is difficult to combine with Python."
"Data visualization needs improvement."
"The documentation needs a proper rework. "
"The pricing needs improvement."
"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 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."
"Weka is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science."
"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 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."
"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."
"Weka could be more stable."
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, Splunk User Behavior Analytics and SAS Analytics. See our KNIME vs. Weka report.
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