Compare SAS Enterprise Miner vs. Weka

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SAS Enterprise Miner Logo
3,283 views|2,498 comparisons
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Read 7 Weka reviews.
2,912 views|2,508 comparisons
Most Helpful Review
Find out what your peers are saying about SAS Enterprise Miner vs. Weka and other solutions. Updated: May 2021.
479,816 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
"The setup is straightforward. Deployment doesn't take more than 30 minutes.""The solution is very good for data mining or any mining issues.""he solution is scalable.""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.""The most valuable feature is that you can use multiple algorithms for creating models and then you can compare the results between them.""Good data management and analytics.""The solution is able to handle quite large amounts of data beautifully."

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"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.""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.""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 mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for 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."

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Cons
"The user interface of the solution needs improvement. It needs to be more visual.""The solution is very stable, but we do have some problems with discrepancies involving SAS not matching with the latest Java versions. It's not stable in cases where SAS tries to run on a different version because SAS doesn't connect with the latest Java update. Once a month we need to restart systems from scratch.""The solution needs an easier interface for the user. The user experience isn't so easy for our clients.""Virtualization could be much better.""The ease of use can be improved. When you are new it seems a bit complex.""The visualization of the models is not very attractive, so the graphics should be improved.""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."

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"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.""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.""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.""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."

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Pricing and Cost Advice
"This solution is for large corporations because not everybody can afford it.""The solution is expensive for an individual, but for an enterprise/institution (purchasing bulk licenses), it is not a high price for the use that will come from it."

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"Currently, I am using an open-source version so I don't know much about the price of this solution."

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Questions from the Community
Top Answer: The technical support is very good.
Top Answer: We'd prefer it if the solution was open source. That would make it less expensive.
Top Answer: We really don't like the protocols the solution offers. The solution is much more complex than other options.
Top Answer: 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… more »
Top Answer: I like how the classification and prediction work. We should use Weka because the path is very big and much better. If there are a lot more lines of code, then we should use another language.
Top Answer: 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. In this case, it would… more »
Ranking
4th
out of 16 in Data Mining
Views
3,283
Comparisons
2,498
Reviews
10
Average Words per Review
391
Rating
7.5
5th
out of 16 in Data Mining
Views
2,912
Comparisons
2,508
Reviews
7
Average Words per Review
1,001
Rating
7.3
Popular Comparisons
Also Known As
Enterprise Miner
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Weka
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Overview
SAS Enterprise Miner is a solution to create accurate predictive and descriptive models on large volumes of data across different sources in the organization. SAS Enterprise Miner offers many features and functionalities for the business analysts to model their data. Some of the business applications are for detecting fraud, minimizing risk, resource demands, reducing asset downtime, campaigns and reduce customer attrition.
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
Offer
Learn more about SAS Enterprise Miner
Learn more about Weka
Sample Customers
Generali Hellas, Gitanjali Group, Gloucestershire Constabulary, GS Home Shopping, HealthPartners, IAG New Zealand, iJET, Invacare
Information Not Available
Top Industries
REVIEWERS
Financial Services Firm57%
Media Company14%
Retailer14%
University14%
VISITORS READING REVIEWS
Computer Software Company24%
Financial Services Firm12%
Comms Service Provider11%
Government7%
VISITORS READING REVIEWS
Comms Service Provider32%
Educational Organization14%
Computer Software Company12%
Financial Services Firm7%
Company Size
REVIEWERS
Small Business25%
Midsize Enterprise33%
Large Enterprise42%
No Data Available
Find out what your peers are saying about SAS Enterprise Miner vs. Weka and other solutions. Updated: May 2021.
479,816 professionals have used our research since 2012.

SAS Enterprise Miner is ranked 4th in Data Mining with 10 reviews while Weka is ranked 5th in Data Mining with 7 reviews. SAS Enterprise Miner is rated 7.6, while Weka is rated 7.2. The top reviewer of SAS Enterprise Miner writes "Good GUI, an easy initial setup, and very flexible". On the other hand, the top reviewer of Weka writes "Relatively stable with excellent accuracy and there's no need to know coding". SAS Enterprise Miner is most compared with IBM SPSS Modeler, Microsoft Azure Machine Learning Studio, Amazon SageMaker, RapidMiner and FICO Model Builder, whereas Weka is most compared with KNIME, IBM SPSS Statistics, IBM SPSS Modeler, SAS Analytics and ELK Elasticsearch. 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.