![]() ![]() In both programs you can scan in files such as Excel, CSV, SAS, STATA, SPSS, XML and many more. On RapidMiner you can scan in all JDBC connections and you can have a direct link with Twitter and Salesforce. With KNIME you have the option to cooperate with Weka, SQL and Java etc. Both programs require you to install an extra plug-in (free) allowing you to invoke R and Python scripts from your flow or process. RapidMiner and KNIME are both able to cooperate with R and Python. I have not encountered a single operation in either program that I wanted to run but couldn’t find. For example, in KNIME a zero variance filter is called a low variance filter so you’ll be looking for a while if you search for “zero”.įurthermore, in both KNIME and RapidMiner you can create loops to make operations run on a large number of variables. It is a drawback that the operations are always called something slightly different to what you expect. gives KNIME a slight advantage because it allows you to be more flexible and to apply more advanced methods and techniques.Īll the possible operations are clearly structured in both programs and you can search in all the possible operations to find what you need. KNIME has a choice of more than 1000 standard operations and RapidMiner even has more than 1500. They also both have very diverse data preparation techniques in their standard package. They are both good at all kinds of data analysis techniques such as decision trees, segmentation analyses, feature selection, neural networks and many more. KNIME and RapidMiner are fairly closely matched when it comes to methods and techniques. So you can see how far you have got at a single glance. And if there is a green light, the operation is complete and you can view the output. If there is an amber light, it is ready to run. If there is a red light, the operation still needs to be configured. Not just because of the clear naming system of the operations, the good use of colours, but I am also a big fan of the traffic lights that KNIME uses in its interface, which indicate what stage of processing each operation is at. ![]() ![]() ![]() Major alternatives for analysis in the open source domain – such as R and Python – do not (yet) offer the user a generic graphical user interface and therefore they are less accessible than KNIME and RapidMiner. In this respect both KNIME and RapidMiner look like SPSS Modeler and SAS Enterprise Miner (and Guide). The graphical user interface means projects look uncluttered, allowing you to make links between projects and making it easier to cooperate on one project. You can drag and drop these to add them to your process on the right ( ). You can then drag and drop these to add them to your flow on the right ( ).īelow you can see operators where you can choose from all kinds of operations. what sort of decision tree you want to run, how you want to split your testing and training set, or how you want to impute your missings.īelow you can see the node repository where you can choose from all kinds of operations. You can then further specify these operations using a menu e.g. Both use a graphical user interface you can drag and drop all kinds of operations to add them to your analysis flow (KNIME) or process (RapidMiner). Let’s start the comparison with the look and feel of the programs. We’ll come back to that later in the article. Of course, both products also have various types of possible upgrades so you can scan in more data or different data, run more analyses or different analyses or receive more support. For KNIME that is the KNIME Analytics Platform and for RapidMiner it is the RapidMiner Community Edition. In this blog we are going to compare the free versions of KNIME and RapidMiner. RapidMiner can be used both by experienced scientists and new analysts alike. RapidMiner went on to become a fast-growing software company with more than 300% growth between 20, which now has established roots in Boston. RapidMiner (Boston) is an analytics program that was originally created at the Technical University of Dortmund in 2001. KNIME is now also used in many other fields such as marketing and customer intelligence and can be used by both new analysts and more technical data scientists alike. This can also be seen in the specific chemical analysis options offered by KNIME. KNIME (Konstanz Information Miner Zurich) is an open source analysis platform that came into being back in 2004 in response to the need for specific analysis tools for the pharmaceutical industry. If you are interested in seeing a comparison between SAS and IBM SPSS (the two other market leaders in Gartner’s 2017 Magic Quadrant) with R and Python, then be sure to read this article. Both KNIME and RapidMiner are both hailed by Gartner as 2017’s market leaders in data science platforms so it is high time to compare these two programs. There is now such a wide variety of software, some open source, for analysing data. ![]()
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