Manifold learning (ML), also known as nonlinear dimension reduction, is a set of methods to
find the low-dimensional structure of data. Dimension reduction for large, high-dimensional …

We present an accelerated algorithm for hierarchical density based clustering. Our new
algorithm improves upon HDBSCAN*, which itself provided a significant qualitative …

Data mining of massive data sets is transforming the way we think about crisis response,
marketing, entertainment, cybersecurity and national intelligence. Collections of documents …

The problem of efficiently finding the best match for a query in a given set with respect to the
Euclidean distance or the cosine similarity has been extensively studied. However, the …

In this paper we develop density estimation trees (DETs), the natural analog of classification
trees and regression trees, for the task of density estimation. We consider the estimation of a …

Advances in Machine Learning and Data Mining for Astronomy Page 1 W ay, Scargle, Chapman
& Hall/CRC Data Mining and Knowledge Discovery Series Advances in Machine Learning …

The Euclidean Minimum Spanning Tree problem has applications in a wide range of fields,
and many efficient algorithms have been developed to solve it. We present a new, fast …

This is an introduction to multicast routing, which is the study of methods for routing from one
source to many destinations, or from many sources to many destinations. Multicast is …

Dimensionality reduction and manifold learning methods such as t-distributed stochastic
neighbor embedding (t-SNE) are frequently used to map high-dimensional data into a two …

The first step in graph-based semi-supervised classification is to construct a graph from input
data. While the k-nearest neighbor graphs have been the de facto standard method of graph …