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Single-level density-based approach has long been widely acknowledged to be a conceptually and mathematically convincing clustering method. In this paper, we propose an algorithm called best-scored clustering forest that can obtain the optimal level and determine corresponding clusters. The terminology best-scored means to select one random tree with the best empirical performance out of a certain number of purely random tree candidates. From the theoretical perspective, we first show that consistency of our proposed algorithm can be guaranteed. Moreover, under certain mild restrictions on the underlying density functions and target clusters, even fast convergence rates can be achieved. Last but not least, comparisons with other state-of-the-art clustering methods in the numerical experiments demonstrate accuracy of our algorithm on both synthetic data and several benchmark real data sets.
This paper presents a brand new nonparametric density estimation strategy named the best-scored random forest density estimation whose effectiveness is supported by both solid theoretical analysis and significant experimental performance. The termino
We propose an algorithm named best-scored random forest for binary classification problems. The terminology best-scored means to select the one with the best empirical performance out of a certain number of purely random tree candidates as each singl
We propose a novel method designed for large-scale regression problems, namely the two-stage best-scored random forest (TBRF). Best-scored means to select one regression tree with the best empirical performance out of a certain number of purely rando
We introduce a density-based clustering method called skeleton clustering that can detect clusters in multivariate and even high-dimensional data with irregular shapes. To bypass the curse of dimensionality, we propose surrogate density measures that
Given a set of empirical observations, conditional density estimation aims to capture the statistical relationship between a conditional variable $mathbf{x}$ and a dependent variable $mathbf{y}$ by modeling their conditional probability $p(mathbf{y}|