Electronic Resource

Linking and Mining Heterogeneous and Multi-view Data

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Penilaian

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This book highlights research in linking and mining data from across varied data sources. The authors focus on recent advances in this burgeoning field of multi-source data fusion, with an emphasis on exploratory and unsupervised data analysis, an area of increasing significance with the pace of growth of data vastly outpacing any chance of labeling them manually. The book looks at the underlying algorithms and technologies that facilitate the area within big data analytics, it covers their applications across domains such as smarter transportation, social media, fake news detection and enterprise search among others. This book enables readers to understand a spectrum of advances in this emerging area, and it will hopefully empower them to leverage and develop methods in multi-source data fusion and analytics with applications to a variety of scenarios.

Includes advances on unsupervised, semi-supervised and supervised approaches to heterogeneous data linkage and fusion;
Covers use cases of analytics over multi-view and heterogeneous data from across a variety of domains such as fake news, smarter transportation and social media, among others;
Provides a high-level overview of advances in this emerging field and empowers the reader to explore novel applications and methodologies that would enrich the field.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
1908002185	EB0002892	006.312 Lin	Central Library (OPAC)	Available

Detail Information

Series Title

-

Call Number

006.312 Lin

Publisher

Switzerland : Springer Cham., 2018

Collation

viii, 343p.:Ill

Language

English

ISBN/ISSN

978-3-030-01872-6

Classification

006.312

Content Type

Ebook

Media Type

-

Carrier Type

online resource

Edition

1

Subject(s)

Data mining

Specific Detail Info

-

Statement of Responsibility

BRF

Other version/related

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