000 aam a22 4500
999 _c210694
_d210694
008 190116b ||||| |||| 00| 0 eng d
020 _a9781482208238
082 _a005.7
_bA7
245 _aApplied biclustering methods for big and high-dimensional data using R
260 _bCRC Press
_c2017
_aLondon
300 _axxv, 401 p.
_bWith index
520 _aAs big data has become standard in many application areas, challenges have arisen related to methodology and software development, including how to discover meaningful patterns in the vast amounts of data. Addressing these problems, Applied Biclustering Methods for Big and High-Dimensional Data Using R shows how to apply biclustering methods to find local patterns in a big data matrix. The book presents an overview of data analysis using biclustering methods from a practical point of view. Real case studies in drug discovery, genetics, marketing research, biology, toxicity, and sports illustrate the use of several biclustering methods. References to technical details of the methods are provided for readers who wish to investigate the full theoretical background. All the methods are accompanied with R examples that show how to conduct the analyses. The examples, software, and other materials are available on a supplementary website. https://www.crcpress.com/Applied-Biclustering-Methods-for-Big-and-High-Dimensional-Data-Using-R/Kasim-Shkedy-Kaiser-Hochreiter-Talloen/p/book/9781482208238
650 _aR (Computer program language)
_9373427
650 _aCluster set theory
_9373428
650 _aBig data
_9373429
700 _aKasim, Adetayo
_eEditor
_9373430
700 _aShkedy, Ziv
_eEditor
_9373431
700 _aKaiser, Sebastian
_eEditor
_9373432
700 _aHochreiter, Sepp
_eEditor
_9373433
700 _aTalloen, Willem
_eEditor
_9373434
942 _2ddc
_cBK