This book addresses some theoretical aspects of semisupervised learning (SSL). The book
is organized as a collection of different contributions of authors who are experts on this topic …

We believe that the cluster assumption is key to successful semi-supervised learning. Based
on this, we propose three semi-supervised algorithms: 1. deriving graph-based distances …

Spectral clustering and path-based clustering are two recently developed clustering
approaches that have delivered impressive results in a number of challenging clustering …

In 2014, a novel clustering algorithm called Density Peak Clustering (DPC) was proposed in
journal Science, which has received great attention in many fields due to its simplicity and …

A knee point is almost always a preferred trade-off solution, if it exists in a bicriteria
optimization problem. In this article, an attempt is made to improve understanding of a knee …

Various graph-based algorithms for semi-supervised learning have been proposed in the
recent literature. They rely on the idea of building a graph whose nodes are data points …

This is a book about (machine) learning from (experimental) data. Many books devoted to
this broad field have been published recently. One even feels tempted to begin the previous …

Similarity calculation is one of the most critical steps of clustering analysis, especially for
arbitrarily formed elongated structures. When it comes to Density Peak Clustering (DPC) …

This paper addresses classification of hyperspectral remote sensing images with kernel-
based methods defined in the framework of semisupervised support vector machines (S 3 …

Learning from imbalanced datasets poses a major challenge in data mining community.
When dealing with imbalanced datasets, conventional classification algorithms generally …