Domain driven data mining

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dc.contributor.author Cao, L
dc.contributor.author Yu, PS
dc.contributor.author Zhang, C
dc.contributor.author Zhao, Y
dc.date.accessioned 2010-05-28T09:36:40Z
dc.date.issued 2010
dc.identifier.citation 2010, pp. 1 - 248
dc.identifier.isbn 9781441957368
dc.identifier.other A1 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.
dc.publisher Springer US
dc.relation.isbasedon 10.1007/978-1-4419-5737-5
dc.title Domain driven data mining
dc.type Book
dc.journal.number en_US
dc.publocation New York, USA en_US
dc.identifier.startpage en_US
dc.identifier.endpage en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080110 Simulation and Modelling
dc.for 080611 Information Systems Theory
dc.personcode 011221
dc.personcode 034535
dc.personcode 998488
dc.personcode 107211
dc.percentage 50 en_US
dc.classification.name Information Systems Theory en_US
dc.classification.type FOR-08 en_US
dc.edition 1 en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords NA en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems


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