"From a user-friendliness perspective, it's a great tool."
"What I like the most is that it works almost out of the box with Random Forest and other Forest nodes."
"The solution is good for teaching, since there is no need to code."
"This solution is easy to use and especially good at data preparation and wrapping."
"The most valuable feature is the data wrangling, which is what I mainly use it for."
"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."
"It's a coding-less opportunity to use AI. This is the major value for me."
"The product is open-source and therefore free to use."
"I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."
"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."
"Working with complicated algorithms in huge datasets is really easy in Weka."
"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 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."
"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."
"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."
"They should look at other vendors like Alteryx that are more user friendly and modern."
"There should be better documentation and the steps should be easier."
"KNIME needs to provide more documentation and training materials, including webinars or online seminars."
"It could input more data acquisitions from other sources and it is difficult to combine with Python."
"The documentation is lacking and it could be better."
"Compared to the other data tools on the market, the user interface can be improved."
"I would prefer to have more connectivity."
"From the point of view of the interface, they can do a little bit better."
"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."
"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."
"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."
"Not particularly user friendly."
"Within the basic Weka tool, I don't see many tools that are available where we can analyze and visualize the data that well."
"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."
"If there are a lot more lines of code, then we should use another language."
Earn 20 points
KNIME is ranked 1st in Data Mining with 14 reviews while Weka is ranked 4th in Data Mining with 7 reviews. KNIME is rated 8.2, while Weka is rated 7.2. The top reviewer of KNIME writes "Good workflow tools, supports Python and R integration". On the other hand, the top reviewer of Weka writes "Relatively stable with excellent accuracy and there's no need to know coding". KNIME is most compared with Alteryx, RapidMiner, Databricks, Microsoft Azure Machine Learning Studio and Dataiku Data Science Studio, whereas Weka is most compared with IBM SPSS Statistics, IBM SPSS Modeler, SAS Analytics, SAS Enterprise Miner and Oracle Advanced Analytics. See our KNIME vs. Weka report.
See our list of best Data Mining vendors.
We monitor all Data Mining reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
