Tiefpreis
CHF167.20
Print on Demand - Exemplar wird für Sie besorgt.
Data Mining for Business Applications presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The book bridges the gap between business expectations and research outputs.
Data Mining for Business Applications presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The contributions mark a paradigm shift from data-centered pattern mining to domain driven actionable knowledge discovery for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in theory and practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future research and development in the dialogue between academia and business.
Presents knowledge, techniques and case studies to bridge the gap between business expectations and research outputs Explores new research issues in data mining, including trust, organizational and social factors Addresses recent applications in areas such as blog mining and social security mining Introduces techniques and methodologies evidenced and validated in real-life enterprise data mining Includes supplementary material: sn.pub/extras
Klappentext
Data Mining for Business Applications presents state-of-the-art data mining research and development related to methodologies, techniques, approaches and successful applications. The contributions of this book mark a paradigm shift from "data-centered pattern mining" to "domain-driven actionable knowledge discovery (AKD)" for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future data mining research and development in the dialogue between academia and business.
Part I centers on developing workable AKD methodologies, including:
domain-driven data mining
post-processing rules for actions
domain-driven customer analytics
the role of human intelligence in AKD
maximal pattern-based cluster
ontology mining
Part II focuses on novel KDD domains and the corresponding techniques, exploring the mining of emergent areas and domains such as:
social security data
community security data
gene sequences
mental health information
traditional Chinese medicine data
cancer related data
blog data
sentiment information
web data
procedures
moving object trajectories
land use mapping
higher education data
flight scheduling
algorithmic asset management
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 are sure to find this a practical and effective means of enhancing their understanding of and using data mining in their own projects.
Zusammenfassung
Data Mining for Business Applications presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The contributions mark a paradigm shift from data-centered pattern mining to domain driven actionable knowledge discovery for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in theory and practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future research and development in the dialogue between academia and business.
Inhalt
Domain Driven KDD Methodology.- to Domain Driven Data Mining.- Post-processing Data Mining Models for Actionability.- On Mining Maximal Pattern-Based Clusters.- Role of Human Intelligence in Domain Driven Data Mining.- Ontology Mining for Personalized Search.- Novel KDD Domains & Techniques.- Data Mining Applications in Social Security.- Security Data Mining: A Survey Introducing Tamper-Resistance.- A Domain Driven Mining Algorithm on Gene Sequence Clustering.- Domain Driven Tree Mining of Semi-structured Mental Health Information.- Text Mining for Real-time Ontology Evolution.- Microarray Data Mining: Selecting Trustworthy Genes with Gene Feature Ranking.- Blog Data Mining for Cyber Security Threats.- Blog Data Mining: The Predictive Power of Sentiments.- Web Mining: Extracting Knowledge from the World Wide Web.- DAG Mining for Code Compaction.- A Framework for Context-Aware Trajectory.- Census Data Mining for Land Use Classification.- Visual Data Mining for Developing Competitive Strategies in Higher Education.- Data Mining For Robust Flight Scheduling.- Data Mining for Algorithmic Asset Management.