This book, drawing on recent literature, highlights several methodologies for the detection of outliers and explains how to apply them to solve several interesting real-life problems. The detection of objects that deviate from the norm in a data set is an essential task in data mining due to its significance in many contemporary applications. More specifically, the detection of fraud in e-commerce transactions and discovering anomalies in network data have become prominent tasks, given recent developments in the field of information and communication technologies and security. Accordingly, the book sheds light on specific state-of-the-art algorithmic approaches such as the community-based analysis of networks and characterization of temporal outliers present in dynamic networks. It offers a valuable resource for young researchers working in data mining, helping them understand the technical depth of the outlier detection problem and devise innovative solutions to address related challenges.

Provides a comprehensive survey of the outlier detection problem including a list of issues, challenges and relevant literature Presents the latest methods for outlier detection with a special focus on categorical data Employs outlier detection principles in contemporary applications such as anomaly detection in network data and characterizing temporal anomalies/outliers in dynamic social networks

Inhalt

Introduction.- Outlier Detection.- Research Issues in Outlier Detection.- Computational Preliminaries.- Outlier Detection in Categorical Data.- Outliers in High Dimensional Data.