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Complex pattern mining = new challen...

Appice, Annalisa.

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Complex pattern mining = new challenges, methods and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Complex pattern mining/ edited by Annalisa Appice ... [et al.].
其他題名:	new challenges, methods and applications /
其他作者:	Appice, Annalisa.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	x, 250 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Data mining. -
電子資源:	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 ... [et al.]. - Cham :Springer International Publishing :2020. - x, 250 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.8801860-949X ;. - Studies in computational intelligence ;v.880..

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:

562972
Data mining.

LC Class. No.: QA76.9.D343 / C667 2020

Dewey Class. No.: 006.312

Complex pattern mining = new challenges, methods and applications /
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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.
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