Novelty detection is the task of classifying test data that differ in some respect from the data
that are available during training. This may be seen as “one-class classification”, in which a …

Anomaly detection is an important problem that has been researched within diverse
research areas and application domains. Many anomaly detection techniques have been …

Semi-supervised learning (SSL) improves model generalization by leveraging massive
unlabeled data to augment limited labeled samples. However, currently, popular SSL …

This textbook explores the different aspects of data mining from the fundamentals to the
complex data types and their applications, capturing the wide diversity of problem domains …

Most objects and data in the real world are of multiple types, interconnected, forming
complex, heterogeneous but often semi-structured information networks. However, most …

Clustering is a widely studied data mining problem in the text domains. The problem finds
numerous applications in customer segmentation, classification, collaborative filtering …

Organizing data into sensible grou**s is one of the most fundamental modes of
understanding and learning. As an example, a common scheme of scientific classification …

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and
methods for mining patterns, knowledge, and models from various kinds of data for diverse …

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 …

This paper addresses several key issues in the ArnetMiner system, which aims at extracting
and mining academic social networks. Specifically, the system focuses on: 1) Extracting …