An integrated framework for density-based cluster analysis, outlier detection, and data
visualization is introduced in this article. The main module consists of an algorithm to …

We propose a method (the 'gap statistic') for estimating the number of clusters (groups) in a
set of data. The technique uses the output of any clustering algorithm (eg K‐means or …

In spite of the current availability of numerous methods of cluster analysis, evaluating a
clustering configuration is questionable without the definition of a true population structure …

The abundance of functional observations in scientific endeavors has led to a significant
development in tools for functional data analysis (FDA). This kind of data comes with several …

Recently, there has been a growing interest in multiway probabilistic clustering. Some
efficient algorithms have been developed for this problem. However, not much attention has …

We study generalized density-based clustering in which sharply defined clusters such as
clusters on lower-dimensional manifolds are allowed. We show that accurate clustering is …

Although Hartigan (1975) had already put forward the idea of connecting identification of
subpopulations with regions with high density of the underlying probability distribution, the …

The authors start their book with basic question: Why is randomization beneficial in the
context of algorithms? Or, say it another way: When random choice is better than …

The goal of clustering is to detect the presence of distinct groups in a dataset and assign
group labels to the observations. Nonparametric clustering is based on the premise that the …

We consider the problem of estimating the boundary of a compact set S⊂ ℝd from a random
sample of points taken from S. We use the Devroye-Wise estimator which is a union of balls …