We performed a comparison between IBM SPSS Modeler and KNIME 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 quality is very good."
"New algorithms are added into every version of Modeler, e.g., SMOTE, random forest, etc. The Derive node is used for the syntax code to derive the data."
"Automated modelling, classification, or clustering are very useful."
"The most valuable features of the IBM SPSS Modeler are visual programming, you don't have to write any code, and it is easy to use. 90 to 95 percent of the use cases, you don't have to fine-tune anything. If you want to do something deeper, for example, create a better neural network, then you have to go into the features and try to fine-tune them. However, the default selection which is made by the tool, it's very practical and works well."
"It's very easy to use. The drag and drop feature makes it very easy when you are building and testing the streams. That's very useful."
"The ease of use in the user interface is the best part of it. The ability to customize some of my streams with R and Python has been very useful to me, I've automated a few things with that."
"It makes pretty good use of memory. There are algorithms take a long time to run in R, and somehow they run more efficiently in Modeler."
"It's a very organized product. It's easy to use."
"The most valuable features of KNIME are its ability to convert your sub-workflow into a node. For example, the workflow has many individual native nodes that can be converted into a single node. This representation has simplified my workflow to a great extent. I can present my workflow in a very compact way."
"The most useful features are the readily available extensions that speed up the work."
"The product is open-source and therefore free to use."
"It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured."
"Usability, and organising workflows in very neat manner. Controlling workflow through variables is something amazing."
"The ETL which helps me to collect, reformat, and load the data from multiple sources into one destination, a storage database."
"It has allowed us to easily implement advanced analytics into various processes."
"Easy to use, stable, and powerful."
"Initial setup of the software was complex, because of our own problems within the government."
"The integration with sources and visualisation needs some improvement. The scalability needs improvement."
"I understand that it takes some time to incorporate some of the new algorithms that have come out in the last few months, in the literature. For example, there is an algorithm based on how ants search for food. And there are some algorithms that have now been developed to complement rules. So that's one of the things that we need to have incorporated into it."
"It is not integrated with Qlik, Tableau, and Power BI."
"I can say the solution is outdated."
"It would be good if IBM added help resources to the interface."
"It is very good, but slow. The slowness may be because we have not finalized all the background information in SPSS. It still needs some tweaking."
"Unstructured data is not appropriate for SPSS Modeler."
"In my environment, I need to access a lot of servers with different characteristics and access methods. Some of my servers have to be accessed using proxy which is not supported by KNIME, so I still need to create the middleware to supply the source of my KNIME configurations."
"The data visualization part is the area most in need of improvement."
"They could add more detailed examples of the functionality of every node, how it works and how we can use it, to make things easier at the beginning."
"KNIME could improve when it comes to large data markets."
"The program is not fit for handling very large files or databases (greater than 1GB); it gets too slow and has a tendency to crash easily."
"It could input more data acquisitions from other sources and it is difficult to combine with Python."
"There are a lot of tools in the product and it would help if they were grouped into classes where you can select a function, rather than a specific tool."
"KNIME's documentation is not strong."
IBM SPSS Modeler is ranked 4th in Data Mining with 38 reviews while KNIME is ranked 1st in Data Mining with 50 reviews. IBM SPSS Modeler is rated 8.0, while KNIME is rated 8.2. The top reviewer of IBM SPSS Modeler writes "Easy to use, quick to learn, and offers many ways to analyze data". On the other hand, the top reviewer of KNIME writes "A low-code platform that reduces data mining time by linking script". IBM SPSS Modeler is most compared with Microsoft Power BI, IBM SPSS Statistics, RapidMiner, Alteryx and SAS Visual Analytics, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Dataiku and Databricks. See our IBM SPSS Modeler vs. KNIME report.
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KNIME. It free, open-source, and you can plug in Java, Python, R, and Matlab. The community is awesome.
I used IBM Modeler several years ago and found it to be effective, but expensive. Fortunately, it was for a commercially funded contract.
For KNIME I have only used it for experimental purposes and found it rather cumbersome but powerful. It is also more cost-effective.
I found RapidMiner more intuitive to learn. However, there is so much choice nowadays that it is difficult to be definitive. In my experience, it largely depended on the quality of the add-on extensions. Clearly, though, at least in universities, the cost is a significant factor.
I am not familiar with KNIME, but the main difference is KNIME is open-source and free.