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Data Mining for Business Applications de Longbing Cao

Data Mining for Business Applications

Editat de Longbing Cao, Philip S. Yu, Chengqi Zhang, Huaifeng Zhang
en Limba Engleză Paperback – 4 noi 2010
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.
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Specificații

ISBN-13: 9781441946355
ISBN-10: 1441946357
Pagini: 324
Ilustrații: XX, 302 p.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.45 kg
Ediția:Softcover reprint of hardcover 1st ed. 2009
Editura: Springer Us
Colecția Springer
Locul publicării:New York, NY, United States

Public țintă

Research

Cuprins

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.

Recenzii

From the reviews:
"This is a compendium of papers written by 58 authors from different countries--including six from the US. … present the full gamut of current research in the field of actionable knowledge discovery (AKD), as it applies to real-world problems. … the intended audience of this book clearly includes industry practitioners, as well. … The editors have culled a wide array of methodologies for and applications of data mining, from the cutting edge of research. This book provides … further the development of actionable systems." (R. Goldberg, ACM Computing Reviews, June, 2009)

Textul de pe ultima copertă

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.

Caracteristici

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