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."I like the way the product visually shows the data pipeline."
"The technical support is very good."
"Good data management and analytics."
"The most valuable feature is the decision tree creation."
"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 solution is very good for data mining or any mining issues."
"The solution is able to handle quite large amounts of data beautifully."
"The most valuable feature is that you can use multiple algorithms for creating models and then you can compare the results between them."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"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."
"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."
"Working with complicated algorithms in huge datasets is really easy in Weka."
"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."
"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."
"The interface is very good, and the algorithms are the very best."
"Virtualization could be much better."
"The solution needs an easier interface for the user. The user experience isn't so easy for our clients."
"The solution is much more complex than other options."
"The product must provide better integration with cloud-native technologies."
"Technical support could be improved."
"The initial setup is challenging if doing it for the first time."
"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 user interface of the solution needs improvement. It needs to be more visual."
"A few people said it became slow after a while."
"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."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."
"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."
"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 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."
"If there are a lot more lines of code, then we should use another language."
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 Cloudera Data Science Workbench, 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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