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020			_a9783319251271 _z978-3-319-25127-1
024	7		_a10.1007/978-3-319-25127-1 _2doi
050		4	_aQA75.5-76.95
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100	1		_aChen, Li M. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aMathematical Problems in Data Science : _bTheoretical and Practical Methods / _cby Li M. Chen, Zhixun Su, Bo Jiang.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aIntroduction: Data Science and BigData Computing -- Overview of Basic Methods for Data Science -- Relationship and Connectivity of Incomplete Data Collection -- Machine Learning for Data Science: Mathematical or Computational -- Images, Videos, and BigData -- Topological Data Analysis -- Monte Carlo Methods and their Applications in Big Data Analysis -- Feature Extraction via Vector Bundle Learning -- Curve Interpolation and Financial Curve Construction -- Advanced Methods in Variational Learning: Segmentation with Intensity Inhomogeneity -- An On-line Strategy of Groups Evacuation From a Convex Region in the Plane -- A New Computational Model of Bigdata.
520			_aThis 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 rec overy, 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.
650		0	_aInformation systems.
650		0	_aComputer Communication Networks.
650		0	_aComputer science.
650	1	4	_aInformation Systems and Communication Service. _0http://scigraph.springernature.com/things/product-market-codes/I18008
650	2	4	_aComputer Communication Networks. _0http://scigraph.springernature.com/things/product-market-codes/I13022
650	2	4	_aMathematics of Computing. _0http://scigraph.springernature.com/things/product-market-codes/I17001
700	1		_aSu, Zhixun. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aJiang, Bo. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
856	4	0	_uhttps://doi.org/10.1007/978-3-319-25127-1 _3Springer eBooks _zOnline access link to the resource
912			_aZDB-2-SCS
999			_c200433876 _d52088
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