Hi Igniters, Currently, I'm working on adoption of clustering algorithms (KMeans/FuzzyCMeans) to the new Partitioned Dataset.
KMeans was adopted without any troubles, but FuzzyCMeans couldn't be adopted so easy 1. It uses local data structures to collect indices of rows presented in dataset. It works with old matrix-style approach, but it doesn't work with the new partitioned dataset (it supports close integration with Ignite Cache and works with any types of data, not only matrices) 2. It doesn't predict fuzzy belonging to the vector of clusters. There is a copy-paste apply() method from the KMeans and it's incorrect behaviour. 3. I found a few bugs with weighted coeffiecient recalculation. Summary, algorithm could be adopted fastly and doesn't work correctly according its specification. I suggest to remove the source files in the current release and return in 2.6 with a few fixes. What do you think? Sincerely, Alexey Zinoviev