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001 u375532
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005 20160812084328.0
007 cr nn 008mamaa
008 110203s2010 gw | s |||| 0|eng d
020 _a9783642175572
_9978-3-642-17557-2
040 _cMX-MeUAM
050 4 _aTA329-348
050 4 _aTA640-643
082 0 4 _a519
_223
100 1 _aHadzic, Fedja.
_eauthor.
245 1 0 _aMining of Data with Complex Structures
_h[recurso electrónico] /
_cby Fedja Hadzic, Henry Tan, Tharam S. Dillon.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2010.
300 _aXX, 328 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v333
505 0 _aTree Mining Problem -- Algorithm Development Issues -- Tree Model Guided Framework -- TMG Framework for Mining Ordered Subtrees -- TMG Framework for Mining Unordered Subtrees -- Mining Distance-Constrained Embedded Subtrees -- Mining Maximal and Closed Frequent Subtrees -- Tree Mining Applications -- Extension of TMG Framework for Mining Frequent Subsequences -- Graph Mining -- New Research Directions.
520 _aMining of Data with Complex Structures: - Clarifies the type and nature of data with complex structure including sequences, trees and graphs - Provides a detailed background of the state-of-the-art of sequence mining, tree mining and graph mining. - Defines the essential aspects of the tree mining problem: subtree types, support definitions, constraints. - Outlines the implementation issues one needs to consider when developing tree mining algorithms (enumeration strategies, data structures, etc.) - Details the Tree Model Guided (TMG) approach for tree mining and provides the mathematical model for the worst case estimate of complexity of mining ordered induced and embedded subtrees. -  Explains the mechanism of the TMG framework for mining ordered/unordered induced/embedded and distance-constrained embedded subtrees. -  Provides a detailed comparison of the different tree mining approaches highlighting the characteristics and benefits of each approach. -  Overviews the implications and potential applications of tree mining in general knowledge management related tasks, and uses Web, health and bioinformatics related applications as case studies. -  Details the extension of the TMG framework for sequence mining - Provides an overview of the future research direction with respect to technical extensions and application areas The primary audience is 3rd year, 4th year undergraduate students, Masters and PhD students and academics. The book can be used for both teaching and research. The secondary audiences are practitioners in industry, business, commerce, government and consortiums, alliances and partnerships to learn how to introduce and efficiently make use of the techniques for mining of data with complex structures into their applications. The scope of the book is both theoretical and practical and as such it will reach a broad market both within academia and industry. In addition, its subject matter is a rapidly emerging field that is critical for efficient analysis of knowledge stored in various domains.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aEngineering mathematics.
650 1 4 _aEngineering.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aTan, Henry.
_eauthor.
700 1 _aDillon, Tharam S.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642175565
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v333
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-17557-2
596 _a19
942 _cLIBRO_ELEC
999 _c203412
_d203412