Computer Science – Artificial Intelligence
Scientific paper
2006-01-02
Computer Science
Artificial Intelligence
Article (10 figures). Changes in 2nd version: dropped supplements in favor of better integrated presentation, better literatur
Scientific paper
Data-based classification is fundamental to most branches of science. While recent years have brought enormous progress in various areas of statistical computing and clustering, some general challenges in clustering remain: model selection, robustness, and scalability to large datasets. We consider the important problem of deciding on the optimal number of clusters, given an arbitrary definition of space and clusteriness. We show how to construct a cluster information criterion that allows objective model selection. Differing from other approaches, our truecluster method does not require specific assumptions about underlying distributions, dissimilarity definitions or cluster models. Truecluster puts arbitrary clustering algorithms into a generic unified (sampling-based) statistical framework. It is scalable to big datasets and provides robust cluster assignments and case-wise diagnostics. Truecluster will make clustering more objective, allows for automation, and will save time and costs. Free R software is available.
No associations
LandOfFree
Truecluster: robust scalable clustering with model selection does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Truecluster: robust scalable clustering with model selection, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Truecluster: robust scalable clustering with model selection will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-40754