ISBN: 9783725863365 - big data mining and analytics with applications (10 Ergebnisse)

Hardback. Zustand: New.

Zustand: New.

Hardback. Zustand: New.

HRD. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Zustand: New. Print on Demand.

Zustand: New. Print on Demand.

Zustand: New. PRINT ON DEMAND.

Hardcover. Zustand: new. Hardcover. This Reprint contains all of the articles accepted and published in the Special Issue titled "Big Data Mining and Analytics with Applications" in the journal Mathematics. It compiles 10 papers related to span predictive modeling for spatiotemporal data (e.g., traffic, air quality, crowd flow), data quality and repair, automated data understanding (e.g., table type prediction), advanced clustering and user segmentation, scalable statistical learning (e.g., Bayesian variable selection), and text mining applications (e.g., document classification, fake review detection). Together, they highlight a strong focus on efficient, robust, and interpretable methods for big data mining and analysis across domains. It is our hope that these contributions will serve as an inspiring source for researchers in the domains of big data analytics and machine learning. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Buch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This Reprint contains all of the articles accepted and published in the Special Issue titled 'Big Data Mining and Analytics with Applications' in the journal Mathematics. It compiles 10 papers related to span predictive modeling for spatiotemporal data (e.g., traffic, air quality, crowd flow), data quality and repair, automated data understanding (e.g., table type prediction), advanced clustering and user segmentation, scalable statistical learning (e.g., Bayesian variable selection), and text mining applications (e.g., document classification, fake review detection). Together, they highlight a strong focus on efficient, robust, and interpretable methods for big data mining and analysis across domains. It is our hope that these contributions will serve as an inspiring source for researchers in the domains of big data analytics and machine learning.…