9781608458806-1608458806-Mining Heterogeneous Information Networks: Principles and Methodologies (Synthesis Lectures on Data Mining and Knowledge Discovery)

Mining Heterogeneous Information Networks: Principles and Methodologies (Synthesis Lectures on Data Mining and Knowledge Discovery)

ISBN-13: 9781608458806
ISBN-10: 1608458806
Edition: 1
Author: Jiawei Han, Yizhou Sun
Publication date: 2012
Publisher: Morgan & Claypool Publishers
Format: Paperback 160 pages
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Book details

ISBN-13: 9781608458806
ISBN-10: 1608458806
Edition: 1
Author: Jiawei Han, Yizhou Sun
Publication date: 2012
Publisher: Morgan & Claypool Publishers
Format: Paperback 160 pages

Summary

Mining Heterogeneous Information Networks: Principles and Methodologies (Synthesis Lectures on Data Mining and Knowledge Discovery) (ISBN-13: 9781608458806 and ISBN-10: 1608458806), written by authors Jiawei Han, Yizhou Sun, was published by Morgan & Claypool Publishers in 2012. With an overall rating of 4.0 stars, it's a notable title among other AI & Machine Learning (Data Mining, Databases & Big Data, Data Processing, Physics, Computer Science) books. You can easily purchase or rent Mining Heterogeneous Information Networks: Principles and Methodologies (Synthesis Lectures on Data Mining and Knowledge Discovery) (Paperback) from BooksRun, along with many other new and used AI & Machine Learning books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

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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