Mining Heterogeneous Information Networks : Principles and Methodologies / by Yizhou Sun, Jiawei Han
By: Sun, Yizhou, autor
Contributor(s): Han, Jiawei, autor
Material type:
E-bookSeries: (Synthesis Lectures on Data Mining and Knowledge Discovery, 2151-0075).Publisher: Cham : Springer International Publishing, 2012Edition: 1st edition 2012.Description: 1 recurso en línea (XI, 196 páginas).ISBN: 9783031019029.Subject: Data mining | Redes de informaciónOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds | |
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LIBRO-E NO PRÉSTAMO
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Madrid Digital Acceso Electrónico (UEM) | Ciencias e Ingeniería | QA76.9.D343 2012 EB (Browse shelf(Opens below)) | Acceso electrónico | eBook.01112490 |
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| QA76.9 .D343 2010 EB Data Mining and Knowledge Discovery Handbook | QA76.9.D343 2010 EB Ensemble Methods in Data Mining : Improving Accuracy Through Combining Predictions | QA76.9.D343 2012 EB Privacy in Social Networks | QA76.9.D343 2012 EB Mining Heterogeneous Information Networks : Principles and Methodologies | QA76.9.D343 2015 EB ICT Innovations 2014 World of Data | QA76.9.D343 2015 EB Data Preprocessing in Data Mining | QA76.9.D343 2015 EB Computational Intelligence in Data Mining - Volume 3 Proceedings of the International Conference on CIDM, 20-21 December 2014 |
Introduction -- Ranking-Based Clustering -- Classification of Heterogeneous Information Networks -- Meta-Path-Based Similarity Search -- Meta-Path-Based Relationship Prediction -- Relation Strength-Aware Clustering with Incomplete Attributes -- User-Guided Clustering via Meta-Path Selection -- Research Frontiers.
Real-world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real-world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge. In this book, we investigate the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, our semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network. This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including: (1) rank-based clustering and classification; (2) meta-path-based similarity search and mining; (3) relation strength-aware mining, and many other potential developments. This book introduces this new research frontier and points out some promising research directions. Table of Contents: Introduction / Ranking-Based Clustering / Classification of Heterogeneous Information Networks / Meta-Path-Based Similarity Search / Meta-Path-Based Relationship Prediction / Relation Strength-Aware Clustering with Incomplete Attributes / User-Guided Clustering via Meta-Path Selection / Research Frontiers.
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