國立虎尾科技大學 |

Complex Pattern Mining = New Challenges, Methods and Applications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Complex Pattern Mining/ edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras.
Reminder of title:	New Challenges, Methods and Applications /
other author:	Appice, Annalisa.
Description:	X, 250 p. 77 illus., 47 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-3-030-36617-9
ISBN:	9783030366179

Complex Pattern Mining = New Challenges, Methods and Applications /
Complex Pattern MiningNew Challenges, Methods and Applications /[electronic resource] :edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras. - 1st ed. 2020. - X, 250 p. 77 illus., 47 illus. in color.online resource. - Studies in Computational Intelligence,8801860-949X ;. - Studies in Computational Intelligence,564.

Eﬃcient 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.

This 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.

ISBN: 9783030366179

Standard No.: 10.1007/978-3-030-36617-9doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Complex Pattern Mining = New Challenges, Methods and Applications /
LDR:03083nam a22004095i 4500 001 1027709
003 DE-He213
005 20201105191728.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030366179 $9 978-3-030-36617-9
024 7 $a 10.1007/978-3-030-36617-9 $2 doi
035 $a 978-3-030-36617-9
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
245 1 0 $a Complex Pattern Mining $h [electronic resource] : $b New Challenges, Methods and Applications / $c edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a X, 250 p. 77 illus., 47 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Studies in Computational Intelligence, $x 1860-949X ; $v 880
505 0 $a Eﬃcient 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 $a This 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.
650 0 $a Computational intelligence. $3 568984
650 0 $a Artificial intelligence. $3 559380
650 0 $a Data mining. $3 528622
650 0 $a Pattern recognition. $3 1253525
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Pattern Recognition. $3 669796
700 1 $a Appice, Annalisa. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1020043
700 1 $a Ceci, Michelangelo. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1108822
700 1 $a Loglisci, Corrado. $e editor. $1 https://orcid.org/0000-0001-5790-8368 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1255188
700 1 $a Manco, Giuseppe. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1255189
700 1 $a Masciari, Elio. $e editor. $1 https://orcid.org/0000-0002-1778-5321 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1255190
700 1 $a Ras, Zbigniew W. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 792946
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030366162
776 0 8 $i Printed edition: $z 9783030366186
776 0 8 $i Printed edition: $z 9783030366193
830 0 $a Studies in Computational Intelligence, $x 1860-949X ; $v 564 $3 1253640
856 4 0 $u https://doi.org/10.1007/978-3-030-36617-9
912 $a ZDB-2-INR
912 $a ZDB-2-SXIT
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)
950 $a Intelligent Technologies and Robotics (R0) (SpringerNature-43728)