Hi, i am hoping you can help me with my problem. I am trying to detect outliers with use of the kmeans algorithm. First I perform the algorithm and choose those object as possible outliers which have a big distance to their cluster center. Instead of using the absolute distance I want to use the relative distance, i.e. the ration of absolute distance of the object to the cluster center and the average distance of all objects of the cluster to their cluster center. The code for outlier detection based on absolute distance is the following:
> # remove species from the data to cluster > iris2 <- iris[,1:4] > kmeans.result <- kmeans(iris2, centers=3) > # cluster centers > kmeans.result$centers > # calculate distances between objects and cluster centers > centers <- kmeans.result$centers[kmeans.result$cluster, ] > distances <- sqrt(rowSums((iris2 - centers)^2)) > # pick top 5 largest distances > outliers <- order(distances, decreasing=T)[1:5] > # who are outliers > print(outliers) But how can I use the relative instead of the absolute distance to find outliers? Thanks in advance. Mario -- View this message in context: http://r.789695.n4.nabble.com/Outlier-Detection-with-k-Means-tp4690098.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
