optics-algorithm

问题 I'm an ELKI beginner, and I've been using it to cluster around 10K lat-lon points from a .csv file. Once I get my settings correct, I'd like to scale up to 1MM points. I'm using the OPTICSXi algorithm with LngLatDistanceFunction I keep reading about "enabling R*-tree index with STR bulk loading" in order to see vast improvements in performance. The tutorials haven't helped me much. Any tips on how I can implement this feature? 回答1: The suggested parameters for using a spatial R* index on 2

问题 I am having a hard time understanding the concept of Ordering in OPTICS Clustering algorithm. I Would be grateful if someone gives a logical and intuitive explanation of the ordering and also explain what res$order does in the following code and what is the reahability plot(which can be obtained by the command 'plot(res)'). library(dbscan) set.seed(2) n <- 400 x <- cbind( x = runif(4, 0, 1) + rnorm(n, sd=0.1), y = runif(4, 0, 1) + rnorm(n, sd=0.1) ) plot(x, col=rep(1:4, time = 100)) res <-

问题 I'm normally an R user (a beginning R user, but I'm starting to get the hang of it). However, I have heard positive things about ELKI--in particular, its speed. I came across this old post "How to group nearby latitude and longitude locations stored in SQL" and the answer posted by Anony-Mousse is similar to what I'd like to do. I would like to be able to replicate each step he has done up to the KML file he has shared on Google Drive. I've downloaded ELKI and am able to run the mini-GUI,

问题 I am trying to do OPTICS-clustering with ELKI (http://elki.dbs.ifi.lmu.de/). I try do run the clustering with the provided gui, but then I get this exception: Error in starting visualizer window. java.lang.NoClassDefFoundError: org/w3c/dom/svg/SVGSVGElement at de.lmu.ifi.dbs.elki.visualization.gui.ResultWindow.<init>(Unknown Source) at de.lmu.ifi.dbs.elki.visualization.gui.ResultVisualizer$1.run(Unknown Source) at [...] Caused by: java.lang.ClassNotFoundException: org.w3c.dom.svg