IBM ILOG CPLEX Optimization Studio vs. RapidMiner

In my opinon, if the problem is less than 5000 variables, one should try to solve with free available solver rather than directly going for a commercial license of IBM CPLEX Optimization Studio. In my opinion, if the priority is not in terms of solving time with higher number of variables, even then one can go for free solvers like CBC, IPOPT, SCIP. In my opinion, if priority is solving time and number of variables is also high, only in that case one should prefer going for a commercial license.

RapidMiner is the best tool to build models on textual data. It is rich in ML algorithms and reduces the need to manually tune the parameters. It automatically optimizes them, thus providing a better solution. RapidMiner again extends great capability for data preparation, its insane connections to almost every data source pulls in the data easily into one environment. And it can comfortably perform data cleaning and process tasks over that. RapidMiner is not so good with image, audio or video data. These data points cannot be used directly in their raw form. They must be transformed into some intermediate form for performing analytics over it. Moreover, there are no connectors to directly pull data from their varied sources. For example, we don't have a connector to read audio data directly from a switch and then convert it to text (although Google speech API is available for audio to text conversion.)

Incentivized

• Linear Programming
• Mixed-Integer Linear Programming
• Non-Linear Convex-Optimization
• Visualization
• Shadow Price Analysis
• Parameter Tuning

• RapidMiner Studio offers a superb user interface with an intuitive workflow paradigm that is very easy to learn.
• RapidMiner Studio’s operators make it a complete and powerful tool for data preprocessing, data visualization, and data mining/analytics.
• RapidMiner Studio provides excellent documentation, countless worked examples, training and support via a large user community.
• Every problem is solved using a sequence of operators.
• Statistical analysis capabilities offered with the T-Test, ANOVA, Grouped ANOVA, and ANOVA Matrix operators.
• Textual data mining operators.
• Web-based and cloud computing capabilities.
• Visualization capabilities.
• Marketplace Extensions – especially Finance And Economics.
• Process portability.

Incentivized

• Data handling from different sources like Note Pad, etc.
• Large size of MILP problems.
• Various parameters to set.

• Wish the tool was more efficient in terms of processing power. The tool takes a lot of CPU processing power, even for a small process on a small data set
• Wish there were more options on charts and graphs to visualize the data

Incentivized

Very fast and user-friendly tool

Incentivized

It's nice to use and with good optimization.

Very use to use and learn

Incentivized

Honestly, to say, I never contacted CPLEX but used its forum to know/clarify any issues I faced.

Compared with MATLAB, CPLEX is a more user-friendly and simpler structure for writing models. This one also has a good return on investment.

The other product like RapidMiner Studio that I have used is WEKA. I decided to use RapidMiner because almost all modelling methods and feature selection methods from the Weka machine learning library are available within RapidMiner. Furthermore, RapidMiner Studio is a visual workflow and therefore it is easier to demonstrate and visualise the processes involves in getting the desired results. Visualization of workflow enhances teaching and learning. RapidMiner is rich with algorithms and online learning materials that can assist students in their self-directed learning on data preparation, machine learning, deep learning, text mining, and predictive analytics. Moreover, RapidMiner repository has more than 1500 machine learning algorithms and functions that students can explore for any case study and assignments. The RapidMIner is also an open platform that can seamlessly integrates with other applications programmed with other programming languages like R and Python.

Incentivized

• Better for price.
• Many model parameters/features.
• No visualization.

• We saved over $100k on our direct mail program by not mailing to those unlikely to respond to our mailings based on our predictive analysis.
• Our CX team has saved countless hours by automating call scripts to isolate key phrases and code each call.

Incentivized