EAN: 9783319251257

MATHEMATICAL PROBLEMS IN DATA SCIENCE - LI M CH...

This book describes current problems in data science and Big Data. Key topics are data
classification  Graph Cut  the Laplacian Matrix  Google Page Rank  efficient algorithms 
hardness of problems  different types of big data  geometric data structures  topological data
processing  and various learning methods. For unsolved problems such as incomplete data
relation and reconstruction  the book includes possible solutions and both statistical and
computational methods for data analysis. Initial chapters focus on exploring the properties of
incomplete data sets and partial-connectedness among data points or data sets. Discussions also
cover the completion problem of Netflix matrix  machine learning method on massive data sets 
image segmentation and video search. This book introduces software tools for data science and
Big Data such MapReduce  Hadoop  and Spark. This book contains three parts. The first part
explores the fundamental tools of data science. It includes basic graph theoretical methods 
statistical and AI methods for massive data sets. In second part  chapters focus on the
procedural treatment of data science problems including machine learning methods  mathematical
image and video processing  topological data analysis  and statistical methods. The final
section provides case studies on special topics in variational learning  manifold learning 
business and financial data recovery  geometric search  and computing models. Mathematical
Problems in Data Science is a valuable resource for researchers and professionals working in
data science  information systems and networks. Advanced-level students studying computer
science  electrical engineering and mathematics will also find the content helpful.

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