000 03849nam a22004935i 4500
710 2 _aSpringerLink (Online service)
_9106996
999 _c118614
_d118614
001 118614
003 ES-MaUEC
005 20230102113903.0
006 a||||fo|||| 00| 0
007 cr nn nnnaamaa
008 200114s2020 gw a o |||| 0|eng d
020 _a9783030366179
024 7 _a10.1007/978-3-030-36617-9
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aQA76.9
_b2020 EB
245 0 0 _aComplex Pattern Mining.
_bNew Challenges, Methods and Applications
_cedited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras.
250 _a1st ed. 2020.
264 1 _aCham
_bSpringer International Publishing :
_bImprint: Springer
_c2020.
300 _a1 recurso en línea (X, 250 páginas)
_b77 ilustraciones, 47 ilustraciones a color.
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _atext file
_bPDF
490 0 _aStudies in Computational Intelligence
_x1860-949X
_v880
490 0 _aIntelligent Technologies and Robotics (Springer-42732)
505 0 _aEfficient Infrequent Pattern Mining using Negative Itemset Tree -- Hierarchical Adversarial Training for Multi-Domain -- Optimizing C-index via Gradient Boosting in Medical Survival Analysis -- Order-preserving Biclustering Based on FCA and Pattern Structures -- A text-based regression approach to predict bug-fix time -- A Named Entity Recognition Approach for Albanian Using Deep Learning -- A Latitudinal Study on the Use of Sequential and Concurrency Patterns in Deviance Mining -- Efficient Declarative-based Process Mining using an Enhanced Framework -- Exploiting Pattern Set Dissimilarity for Detecting Changes in Communication Networks -- Classification and Clustering of Emotive Microblogs in Albanian: Two User-Oriented Tasks.
520 3 _aThis book discusses the challenges facing current research in knowledge discovery and data mining posed by the huge volumes of complex data now gathered in various real-world applications (e.g., business process monitoring, cybersecurity, medicine, language processing, and remote sensing). The book consists of 14 chapters covering the latest research by the authors and the research centers they represent. It illustrates techniques and algorithms that have recently been developed to preserve the richness of the data and allow us to efficiently and effectively identify the complex information it contains. Presenting the latest developments in complex pattern mining, this book is a valuable reference resource for data science researchers and professionals in academia and industry.
988 _aPrimersemestre_2020_Robotics
650 7 _2embne
_aData mining
_9162648
700 1 _aAppice, Annalisa.
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aCeci, Michelangelo.
_eeditor
_0(orcid)0000-0002-6690-7583
_1https://orcid.org/0000-0002-6690-7583
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aLoglisci, Corrado.
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aManco, Giuseppe.
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aMasciari, Elio.
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aRas, Zbigniew W.
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783030366162
776 0 8 _iPrinted edition:
_z9783030366186
776 0 8 _iPrinted edition:
_z9783030366193
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-36617-9
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
_n0
998 _b03/2020
_dz
_ek
_zSI