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001 978-3-642-40837-3
003 DE-He213
005 20200421111701.0
007 cr nn 008mamaa
008 130924s2014 gw | s |||| 0|eng d
020 _a9783642408373
_9978-3-642-40837-3
024 7 _a10.1007/978-3-642-40837-3
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aData Mining and Knowledge Discovery for Big Data
_h[electronic resource] :
_bMethodologies, Challenge and Opportunities /
_cedited by Wesley W. Chu.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2014.
300 _aX, 311 p. 99 illus., 29 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Big Data,
_x2197-6503 ;
_v1
505 0 _aAspect and Entity Extraction for Opinion Mining -- Mining Periodicity from Dynamic and Incomplete Spatiotemporal Data -- Spatio-Temporal Data Mining for Climate Data: Advances, Challenges -- Mining Discriminative Subgraph Patterns from Structural Data -- Path Knowledge Discovery: Multilevel Text Mining as a Methodology for Phenomics -- InfoSearch: A Social Search Engine -- Social Media in Disaster Relief: Usage Patterns, Data Mining Tools, and Current Research Directions -- A Generalized Approach for Social Network Integration and Analysis with Privacy Preservation -- A Clustering Approach to Constrained Binary Matrix Factorization.
520 _aThe field of data mining has made significant and far-reaching advances over the past three decades.  Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease.  Further, the data characteristics of the problems have also grown from static to dynamic and spatiotemporal, complete to incomplete, and centralized to distributed, and grow in their scope and size (this is known as big data). The effective integration of big data for decision-making also requires privacy preservation. The contributions to this monograph summarize the advances of data mining in the respective fields. This volume consists of nine chapters that address subjects ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction via binary matrix factorization.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aChu, Wesley W.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642408366
830 0 _aStudies in Big Data,
_x2197-6503 ;
_v1
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-40837-3
912 _aZDB-2-ENG
942 _cEBK
999 _c54987
_d54987