Top 8 Data Mining Tools
KNIMEIBM SPSS StatisticsIBM SPSS ModelerWekaSAS Enterprise MinerSAS AnalyticsOracle Advanced AnalyticsIBM Watson Explorer
The workflows that you build in KNIME can be transferable and this process is a lot easier than with other types of technological setups.
I was able to apply basic algorithms through just dragging and dropping.
The most valuable features are the small learning curve and its ability to hold a lot of data.
SPSS is quite robust and quicker in terms of providing you the output.
The supervised models are valuable. It is also very organized and easy to use.
You take two quarters and compare them and this tool is ideal because it gives you a lot of visibility on the before and after.
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 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.
All of the data analytics features in SAS Analytics are valuable to us since we're using them daily across our entire analytics team.
It's very easy to use once you learn it.
When needed, we will work closely with Oracle support and implement their workaround in our application.
The dashboard interface is intuitive and the user is able to interact with it to receive good results from the analytic.
I have found the auto-generated document very useful as well as the main keywords that are highlighted, which are used for the search functionality within IBM Watson Explorer.