Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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. .

Data mining. --- Engineering. --- Computational Intelligence. --- Data Mining and Knowledge Discovery. --- Data-driven Science, Modeling and Theory Building. --- Construction --- Industrial arts --- Technology --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Computational intelligence. --- Sociophysics. --- Econophysics. --- Economics --- Statistical physics --- Mathematical sociology --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Statistical methods

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)