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."Valuable features include visual workflow creation, workflow variables (parameterisation), automatic caching of all intermediate data sets in the workflow, scheduling with the server."
"We can deploy the solution in a cluster as well."
"It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop."
"There are a lot of connectors available in KNIME."
"The most useful features are the readily available extensions that speed up the work."
"It is a stable solution...It is a scalable solution."
"I would rate the stability of KNIME a ten out of ten."
"It can handle an unlimited amount of data, which is the advantage of using Knime."
"I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."
"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."
"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 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."
"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."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"There are many options where you can fill all of the data pre-processing options that you can implement when you're importing the data. You can also normalize the data and standardize it in an easier way."
"The dynamic column name feature could be improved. When attempting to automate processes involving columns, such as with companies, it becomes difficult to achieve the same result when we make changes."
"It needs more examples, use cases, and MOOC to learn, especially with respect to the algorithms and how to practically create a flow from end-to-end."
"System resource usage. Knime will occupy total system RAM size and other applications will hang."
"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."
"To enhance accessibility and user-friendliness, there is a need for improvements in the interface and usability of deep learning and large-scale learning languages."
"The most difficult part of the solution revolves around its areas concerning machine learning and deep learning."
"The documentation is lacking and it could be better."
"One area that could be improved is increasing awareness and adoption of KNIME among organizations. Despite its capabilities, it is not as well-known as other tools. The advertising and marketing efforts to reach out to companies and universities have not been very successful."
"A few people said it became slow after a while."
"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."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."
"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."
"Not particularly user friendly."
"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."
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 IBM SPSS Modeler, 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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