Outlier detection taking lot of time and not giving any results

Question

I am using Automodel feature in Rapidminer educational license version. My processor is 6 core, i7-8750h with 16 GB ram.The dataset have more than 30000 rows and 12 columns . It have numeric and text data both. I tried running using the best features(as detected by Auto model) and am using the distance based outlier method. The model ran for more than 18 hours but still processing. 
I have installed the outlier detection extension too still it didn't help.
How can I solve this issue?  Is it because the educational version uses only 1 core? 
Kindly help

jacobcybulski · Answer

@sgenzer I have updated the example to make it more complete to show that the anomaly model can be trained and used either for large data sets of for deployment. Please feel free to add this example. And yes, it is lightning fast :)  I could not figure out how to delete an older attachment though :(

sgenzer · Answer

@jacobcybulski I like this...can I add it to my list of processes to add to the Community Repo?

jacobcybulski · Answer

I have just created a similar kind of a model using 130,000 wine reviews from Kaggle. See this process (also attached as RMP):

Note that it is all in the preparation of data, e.g. make sure you have no missing values, numeric values are preferred and I have normalized my attributes. I have split all data into two parts, i.e. first is a smaller data set of 40K examples used for creating an anomaly model and the second part is a larger data set of the remaining 90K examples, where the anomalies I found by using the pre-trained anomaly model. To visualize anomalies, I've created a PCA model, marked examples with high anomaly score as outliers and then plotted both, see below (40K first and 90K second):

It all took less than 5 minutes on my old laptop with 16Gb of RAM.If it does not work for you, try upgrading your RM to version 9.2+, as it fixes some bugs which you may still have in your older version of RM?Good luck -- JacobP.S. This process can be used for finding anomalies in very large data sets 
(you'd have to join the two steams of processing) or for deploying an 
anomaly model for new data (the bottom part can be placed in a separate 
process but you'd have to save your pre-processing models). P.P.S. I have included the improved version of the process as V2, unfortunately I could not figure oout how to delete an older attachment...