Oscar, Sounds like Ignite ML is a perfect fit for your task. Our ML expert will help you to come up with a clean solution once the holidays season is over.
In general, will you be able to write a blog post on how Ignite ML is used for your task once the issues are addressed? -- Denis On Wed, Jan 2, 2019 at 11:25 PM otorreno <oscar.torr...@shapelets.io> wrote: > Denis, > > We have some metadata stored in an Ignite Cache where each row describes a > certain data series, and each column is a property (could be actually of > any > type: strings, doubles, etc.). You can think about it as a table describing > our data series. This table might be potentially quite big, given a high > number of series and properties. > > Based on this table we would like to clusterize our data using different > algorithms (e.g. k-means, decision tree). > > I started looking at it and I liked pretty much the way you have done the > pre-processing pipeline for feature selection, transformation, > normalization > and scaling. The only stone I found on my way was the BinaryObject problem > I > mentioned. > > In fact I made it work as I described in my first post, but with a dirty > solution as I didn't find the way to access the keepBinary property of the > cache used as input. In any case, I will be glad to help in finding a clean > solution to the problem if needed. > > Best, > Oscar > > > > -- > Sent from: http://apache-ignite-developers.2346864.n4.nabble.com/ >
