Transparent Data Mining for Big and Small Data

• Corporate Author: SpringerLink (Online service)
• Other Authors: Cerquitelli, Tania (Editor), Quercia, Daniele (Editor), Pasquale, Frank (Editor)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Series: Studies in Big Data, 32
• Subjects: Computer science.; Big data.; Algorithms.; Data mining.; Computer simulation.; International law.; Intellectual property > Law and legislation.; Complexity, Computational.; Computer Science.; Data Mining and Knowledge Discovery.; International IT and Media Law, Intellectual Property Law.; Algorithm Analysis and Problem Complexity.; Complexity.; Simulation and Modeling.; Big Data/Analytics.

This book focuses on new and emerging data mining solutions that offer a greater level of transparency than existing solutions. Transparent data mining solutions with desirable properties (e.g. effective, fully automatic, scalable) are covered in the book. Experimental findings of transparent soluti...