We performed a comparison between SAS Enterprise Miner 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."The setup is straightforward. Deployment doesn't take more than 30 minutes."
"The technical support is very good."
"I like the way the product visually shows the data pipeline."
"Most of the features, especially on the data analysis tool pack, are really good. The way they do clustering and output is great. You can do fairly elaborate outputs. The results, the ensembles, all of these, are fantastic."
"The most valuable feature is the decision tree creation."
"Good data management and analytics."
"The solution is able to handle quite large amounts of data beautifully."
"The solution is very good for data mining or any mining issues."
"It doesn’t cost anything to use the 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."
"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 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'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."
"I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."
"Virtualization could be much better."
"The product must provide better integration with cloud-native technologies."
"Technical support could be improved."
"While I don't personally need tutorials, I can't say that it wouldn't be helpful for others to have some to help them navigate and operate the system."
"The solution is much more complex than other options."
"The user interface of the solution needs improvement. It needs to be more visual."
"The initial setup is challenging if doing it for the first time."
"The ease of use can be improved. When you are new it seems a bit complex."
"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."
"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."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."
"Weka is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science."
"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."
SAS Enterprise Miner is ranked 6th in Data Mining with 13 reviews while Weka is ranked 2nd in Data Mining with 14 reviews. SAS Enterprise Miner is rated 7.6, while Weka is rated 7.6. The top reviewer of SAS Enterprise Miner writes "A stable product that is easy to deploy and can be used for structured and unstructured data mining". On the other hand, the top reviewer of Weka writes "Open source, good for basic data mining use cases except for the visualization results". SAS Enterprise Miner is most compared with SAS Visual Analytics, IBM SPSS Modeler, RapidMiner, Microsoft Azure Machine Learning Studio and SAS Visual Data Mining and Machine Learning, whereas Weka is most compared with KNIME, IBM SPSS Statistics, IBM SPSS Modeler and Oracle Advanced Analytics. See our SAS Enterprise Miner vs. Weka report.
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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.
