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SAS Enterprise Miner vs Weka comparison

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Buyer's Guide
SAS Enterprise Miner vs. Weka
July 2022
Find out what your peers are saying about SAS Enterprise Miner vs. Weka and other solutions. Updated: July 2022.
622,645 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 technical support is very good.""I found the ease of use of the solution the most valuable. Additionally, other valuable features include: the user interface, power to extract data, compatibility with other technologies (specifically with PS400), and automation of several tasks."

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"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 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.""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.""It doesn’t cost anything to use the product.""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.""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.""I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."

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Cons
"The initial setup is challenging if doing it for the first time.""The solution is much more complex than other options."

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"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.""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 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.""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.""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.""If there are a lot more lines of code, then we should use another language."

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Pricing and Cost Advice
  • "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."
  • More Weka Pricing and Cost Advice →

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    Questions from the Community
    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
    8th
    out of 16 in Data Mining
    Views
    2,317
    Comparisons
    1,823
    Reviews
    2
    Average Words per Review
    400
    Rating
    6.5
    4th
    out of 16 in Data Mining
    Views
    3,902
    Comparisons
    2,155
    Reviews
    7
    Average Words per Review
    1,001
    Rating
    7.6
    Comparisons
    Also Known As
    Enterprise Miner
    Learn More
    Weka
    Video Not Available
    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 Company17%
    Financial Services Firm16%
    Comms Service Provider14%
    Government6%
    VISITORS READING REVIEWS
    Comms Service Provider28%
    Educational Organization14%
    Computer Software Company10%
    University9%
    Company Size
    REVIEWERS
    Small Business25%
    Midsize Enterprise33%
    Large Enterprise42%
    VISITORS READING REVIEWS
    Small Business22%
    Midsize Enterprise14%
    Large Enterprise64%
    VISITORS READING REVIEWS
    Small Business22%
    Midsize Enterprise20%
    Large Enterprise58%
    Buyer's Guide
    SAS Enterprise Miner vs. Weka
    July 2022
    Find out what your peers are saying about SAS Enterprise Miner vs. Weka and other solutions. Updated: July 2022.
    622,645 professionals have used our research since 2012.

    SAS Enterprise Miner is ranked 8th in Data Mining with 2 reviews while Weka is ranked 4th in Data Mining with 8 reviews. SAS Enterprise Miner is rated 6.6, while Weka is rated 7.6. The top reviewer of SAS Enterprise Miner writes "Good technical support but too complex and not open-source". 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 SAS Visual Analytics, IBM SPSS Modeler, Microsoft Azure Machine Learning Studio, RapidMiner and Databricks, whereas Weka is most compared with KNIME, IBM SPSS Statistics, IBM SPSS Modeler, SAS Analytics and Elastic Enterprise Search. 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.