Le 20/10/2010 10:04, VanIngen, Erik (FIPS) a écrit : > Good morning! > > I need to to cluster analysis on values like this: > 1.814263985 -0.633923297 > 2.501153739 -0.559033358 > 2.408755862 -0.509902975 > 1.935495243 -0.330554484 > 0.728818279 -0.169024633 > -0.523861032 0.110392311 > > I can use EuclideanIntegerPoint, but than I have to convert the values to > integers and would loose precission. So my trick would be to multiply with > 1000, cluster and multiply the values with 0.001. Would that be a valid > approach from a methodology point of view? > > Are there any plans to develop a EuclideanDoublePoint?
The K-means++ clusterer can handle any implementation of the Clusterable interface. The intent is to allow users to provide their own class to suit their needs. The EuclideanIntegerPoint can be seen as a simple reference implementation. There are no plans to add other implementations yet. In order to avoid data duplication, I would suggest that your existing class that already holds the values implements the Clusterable interface by itself. This way, you can directly provide your own data to K-means++. Hope this helps Luc > > Cheers, > Erik van Ingen > > --------------------------------------------------------------------- > To unsubscribe, e-mail: [email protected] > For additional commands, e-mail: [email protected] > --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
