A single modification to a mode-seeking clustering algorithm proposed by Koontz, Narendra, and Fukunaga is shown to generate a novel clustering and to provide an indication of cluster stability. The modified method should provide better clusterings for ``uniform, touching'' clusters than the original, although the original should work better than the modified method for ``touching Gaussian'' clusters. Suitable ranges for the clustering parameters of both methods are investigated. Since the modification requires changing only one line of the original algorithm, two clusterings can be obtained for the price of one coding.

The intrinsic dimensionality of a set of patterns is important in determining an appropriate number of features for representing the data and whether a reasonable two- or three-dimensional representation of the data exists. We propose an intuitively appealing, noniterative estimator for intrinsic dimensionality which is based on nearneighbor information. We give plausible arguments supporting the consistency of this estimator. The method works well in identifying the true dimensionality for a variety of artificial data sets and is fairly insensitive to the number of samples and to the algorithmic parameters. Comparisons between this new method and the global eigenvalue approach demonstrate the utility of our estimator.