Abstract Semi-supervised clustering (SSC), a technique integrating semi-supervised
learning and clustering analysis, incorporates the given prior information (eg, class labels …

Clustering algorithms are a class of unsupervised machine learning (ML) algorithms that
feature ubiquitously in modern data science, and play a key role in many learning-based …

Abstract Open World Object Detection (OWOD) is a challenging computer vision problem
that requires detecting unknown objects and gradually learning the identified unknown …

Visual analytics systems combine machine learning or other analytic techniques with
interactive data visualization to promote sensemaking and analytical reasoning. It is through …

As recently as the 1990s, studies showed that most people preferred getting information
from other people rather than from information retrieval systems. Of course, in that time …

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 …

Discovering new intents is a crucial task in dialogue systems. Most existing methods are
limited in transferring the prior knowledge from known intents to new intents. These methods …

This chapter characterises the scope of this book. It explains the reasons why one should be
interested in cluster analysis, lists major application areas, basic theoretical and practical …

Clustering is an important topic in machine learning and data mining. Recently, deep
clustering, which learns feature representations for clustering tasks using deep neural …

Unsupervised clustering can be significantly improved using supervision in the form of
pairwise constraints, ie, pairs of instances labeled as belonging to same or different clusters …