🎉 🎉 RAPIDMINER 9.10 IS OUT!!! 🎉🎉
Download the latest version helping analytics teams accelerate time-to-value for streaming and IIOT use cases.
"[solved]Problem when doing K-means cluster for big table"
I have a table containing 9000 tupples and each with 60 attributes, the id and attributes are all integers values. I imported it into the repository using excel and want to do the K-means cluster using cosine similarity. I assigned 1G memory to Rapidminer, but there's still problem, I wait for 3 hours but there's no result. In the command line there's words saying exception of java memory. When I use only 30 tupples to run the clustering , it works fine. But my computer only has 1G available free memory, is there any way to solve this problem in my computer and make it successful?