A comparison of bandwidth selectors for mean shift clustering
This work addresses bandwidth selection for researchers using mean shift clustering, but it is incremental as it applies existing methods to a new context without introducing novel techniques.
The paper tackled the problem of selecting bandwidths for mean shift clustering by comparing automatic bandwidth selectors originally designed for density gradient estimation, and found that methods like cross-validation and plug-in selectors are useful for cluster analysis via this algorithm.
We explore the performance of several automatic bandwidth selectors, originally designed for density gradient estimation, as data-based procedures for nonparametric, modal clustering. The key tool to obtain a clustering from density gradient estimators is the mean shift algorithm, which allows to obtain a partition not only of the data sample, but also of the whole space. The results of our simulation study suggest that most of the methods considered here, like cross validation and plug in bandwidth selectors, are useful for cluster analysis via the mean shift algorithm.