Domain driven data mining

Cao, L; Yu, PS; Zhang, C; Zhao, Y

Domain driven data mining

Cao, L

Yu, PS Zhang, C

Zhao, Y

Permalink

Publication Type:: Book
Citation:: 2010, pp. 1 - 248
Issue Date:: 2010-12-01

Closed Access

	Filename	Description	Size
	2009001489OK_Cao.pdf		5.77 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Yu, PS	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Zhao, Y	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	2010, pp. 1 - 248	en_US
dc.identifier.isbn	9781441957368	en_US
dc.identifier.uri	http://hdl.handle.net/10453/7768
dc.description.abstract	In the present thriving global economy a need has evolved for complex data analysis to enhance an organization's production systems, decision-making tactics, and performance. In turn, data mining has emerged as one of the most active areas in information technologies. Domain Driven Data Mining offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. About this book: •Enhances the actionability and wider deployment of existing data-centered data mining through a combination of domain and business oriented factors, constraints and intelligence. •Examines real-world challenges to and complexities of the current KDD methodologies and techniques. •Details a paradigm shift from "data-centered pattern mining" to "domain driven actionable knowledge discovery" for next-generation KDD research and applications. •Bridges the gap between business expectations and research output through detailed exploration of the findings, thoughts and lessons learned in conducting several large-scale, real-world data mining business applications •Includes techniques, methodologies and case studies in real-life enterprise data mining •Addresses new areas such as blog mining Domain Driven Data Mining is suitable for researchers, practitioners and university students in the areas of data mining and knowledge discovery, knowledge engineering, human-computer interaction, artificial intelligence, intelligent information processing, decision support systems, knowledge management, and KDD project management. © Springer Science+Business Media, LLC 2010. All rights reserved.	en_US
dc.relation.isbasedon	10.1007/978-1-4419-5737-5	en_US
dc.title	Domain driven data mining	en_US
dc.type	Book
utslib.for	080110 Simulation and Modelling	en_US
utslib.for	080611 Information Systems Theory	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In the present thriving global economy a need has evolved for complex data analysis to enhance an organization's production systems, decision-making tactics, and performance. In turn, data mining has emerged as one of the most active areas in information technologies. Domain Driven Data Mining offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. About this book: •Enhances the actionability and wider deployment of existing data-centered data mining through a combination of domain and business oriented factors, constraints and intelligence. •Examines real-world challenges to and complexities of the current KDD methodologies and techniques. •Details a paradigm shift from "data-centered pattern mining" to "domain driven actionable knowledge discovery" for next-generation KDD research and applications. •Bridges the gap between business expectations and research output through detailed exploration of the findings, thoughts and lessons learned in conducting several large-scale, real-world data mining business applications •Includes techniques, methodologies and case studies in real-life enterprise data mining •Addresses new areas such as blog mining Domain Driven Data Mining is suitable for researchers, practitioners and university students in the areas of data mining and knowledge discovery, knowledge engineering, human-computer interaction, artificial intelligence, intelligent information processing, decision support systems, knowledge management, and KDD project management. © Springer Science+Business Media, LLC 2010. All rights reserved.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/7768