Dear listers: I have an idea to do the outlier detection and I need to use R to implement it first. Here I hope I can get some input from all the guru's here.
I select distance-based approach--- step 1: calculate the distance of any two rows for a dataframe. considering the scaling among different variables, I choose mahalanobis, using variance as scaler. step 2: Let k be the number of points in one "cluster". K is decided by answering the following question: how many neighbors a point needs for not being an outlier. for each point, get the smallest (k-1) distances from step1. Among the (k-1) distances of each point, get the max for the point. step 3: get the distribution of those max for all the points. Thus, the multivariate problem becomes a univariate one. Then the outlier in those max's will define the outlier of the point. My question is: 1. I don't know if using mahalanobis is proper or not since most clustering algorithms implemented in R (like pam or clara) use euclidean or mahattan. 2. Is there a way to get the mahalanobis distance matrix for any two rows of a dataframe or matrix? 3. My approach does allow a point belonging to more than one k-cluster. Is there similar algorithm in R or published? Thanks for any suggestions, weiwei -- Weiwei Shi, Ph.D "Did you always know?" "No, I did not. But I believed..." ---Matrix III ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
