Clustering and outlier detection using isoperimetric number of trees ?
A new graph-cut based, O ( n log n ) time, data clustering algorithm is introduced. A precise definition is proposed for the concept of an outlier-set. The outlier profile of a data-set is defined and hardness results are proved. Approximation algorithms are given to extract clusters and outliers at the same time. Comparative performance analysis is presented on benchmarks and handmade examples.
outlier detection data clustering isoperimetric constant cheeger constant normalized cut graph partitioning perceptual grouping