Data Mining for Business Applications

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Cao, Longbing (Επιμελητής έκδοσης), Yu, Philip S. (Επιμελητής έκδοσης), Zhang, Chengqi (Επιμελητής έκδοσης), Zhang, Huaifeng (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Boston, MA : Springer US, 2009.
• Θέματα: Computer science.; Leadership.; Computers.; Data mining.; Information storage and retrieval.; Artificial intelligence.; Computer Science.; Data Mining and Knowledge Discovery.; Business Strategy/Leadership.; Information Storage and Retrieval.; Artificial Intelligence (incl. Robotics).; Computing Methodologies.; Models and Principles.

 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. 

 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.