Domain Driven Data Mining

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 offer...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Cao, Longbing (Συγγραφέας), Yu, Philip S. (Συγγραφέας), Zhang, Chengqi (Συγγραφέας), Zhao, Yanchang (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Boston, MA : Springer US, 2010.
Έκδοση:First.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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020 |a 9781441957375  |9 978-1-4419-5737-5 
024 7 |a 10.1007/978-1-4419-5737-5  |2 doi 
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100 1 |a Cao, Longbing.  |e author. 
245 1 0 |a Domain Driven Data Mining  |h [electronic resource] /  |c by Longbing Cao, Philip S. Yu, Chengqi Zhang, Yanchang Zhao. 
250 |a First. 
264 1 |a Boston, MA :  |b Springer US,  |c 2010. 
300 |a XVI, 248 p.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
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505 0 |a Challenges and Trends -- Methodology -- Ubiquitous Intelligence -- Knowledge Actionability -- AKD Frameworks -- Combined Mining -- Agent-Driven Data Mining -- Post Mining -- Mining Actionable Knowledge on Capital Market Data -- Mining Actionable Knowledge on Social Security Data -- Open Issues and Prospects -- Reading Materials. 
520 |a 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. 
650 0 |a Computer science. 
650 0 |a Information technology. 
650 0 |a Business  |x Data processing. 
650 0 |a Business mathematics. 
650 0 |a Data mining. 
650 0 |a Information storage and retrieval. 
650 1 4 |a Computer Science. 
650 2 4 |a Data Mining and Knowledge Discovery. 
650 2 4 |a Information Storage and Retrieval. 
650 2 4 |a Business Mathematics. 
650 2 4 |a IT in Business. 
650 2 4 |a Information Systems Applications (incl. Internet). 
700 1 |a Yu, Philip S.  |e author. 
700 1 |a Zhang, Chengqi.  |e author. 
700 1 |a Zhao, Yanchang.  |e author. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9781441957368 
856 4 0 |u http://dx.doi.org/10.1007/978-1-4419-5737-5  |z Full Text via HEAL-Link 
912 |a ZDB-2-SCS 
950 |a Computer Science (Springer-11645)