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Produktbild: Complex Data Analytics with Formal Concept Analysis

Complex Data Analytics with Formal Concept Analysis

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

30.06.2022

Abbildungen

XXV, 260 p. 87 illus., 27 illus. in color.

Herausgeber

Rokia Missaoui + weitere

Verlag

Springer

Seitenzahl

260

Maße (L/B/H)

24,1/16/2,2 cm

Gewicht

548 g

Auflage

22001 Auflage 1st edition 2022

Sprache

Englisch

ISBN

978-3-030-93277-0

Beschreibung

Portrait


Rokia Missaoui
obtained her Ph.D. in Computer Science from the University of Montreal in 1988 and has been a university professor in Canada for more than thirty years. She is currently a full professor in the Department of Computer Science and Engineering at the University of Quebec in Outaouais (UQO). Before joining UQO in 2002, she was a professor at the University of Quebec in Montreal (UQAM) for fifteen years. She leads the LARIM laboratory at UQO and is a member of the LATECE laboratory at UQAM. Since the beginning of her career, she has been involved in several research projects funded by granting agencies and industrial partners. Her research interests and teaching activities are currently focused on advanced databases, data mining and warehousing, machine learning, social network analysis and big data analytics.
Léonard Kwuida
graduated in mathematics at Université de Yaoundé I. He hold a PhD in algebra and logic from TU Dresden. He currently works at the Department of Business, Bern University of Applied Sciences, where he teaches mathematics and statistics. He does research in Formal Concept Analysis and related algebraic structures. He has (co-)authored more than thirty papers published in peer-reviewed journals, conferences and workshops proceedings. Dr. Kwuida has co-edited four proceedings and published one book. He also serves in the Program Committee of major conferences in FCA.



Talel Abdessalem
is a Professor at Télécom Paris (Institut Polytechnique de Paris). He held the Big Data and Market Insights Chair at Télécom ParisTech from 2013 to 2017. He is heading the Information and Communication (LTCI) research center since 2017 and Dean of Research at Télécom Paris since 2018. His main research achievements are on version control, change detection and querying of XML documents, information extraction from the structured web, and social networks analysis. His current research interests are in recommender systems and predictive analytics. He participated in several national and European research projects. He co-supervised a dozen of Ph. D. students. He received a Ph. D. in Computer Science from the University Paris-Dauphine and a Habilitation degree (HDR) from the University Pierre & Marie Curie. He spent two years as an assistant professor at the University Paris-Dauphine (PSL), before joining Telecom Paris (formerly ENST) in 1998.



Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

30.06.2022

Abbildungen

XXV, 260 p. 87 illus., 27 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

260

Maße (L/B/H)

24,1/16/2,2 cm

Gewicht

548 g

Auflage

22001 Auflage 1st edition 2022

Sprache

Englisch

ISBN

978-3-030-93277-0

Herstelleradresse

Springer-Verlag GmbH
Europaplatz 3
69115 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Complex Data Analytics with Formal Concept Analysis
  • Chapter. 1.- Formal Concept Analysis and Extensions for Complex Data Analytics.- Chapter. 2.- Conceptual Navigation in Large Knowledge Graphs.- Chapter. 3.- FCA2VEC: Embedding Techniques for Formal Concept Analysis.- Chapter. 4.- Analysis of Complex and Heterogeneous Data using FCA and Monadic Predicates.- Chapter. 5.- Dealing with Large Volumes of Complex Relational Data using RCA.- Chapter. 6.- Computing Dependencies using FCA.- Chapter. 7.- Leveraging Closed Patterns and Formal Concept Analysis for Enhanced Microblogs Retrieval.- Chapter. 8.- Scalable Visual Analytics in FCA.- Chapter. 9.- Formal methods in FCA and Big Data.- Chapter. 10.- Towards Distributivity in FCA for Phylogenetic Data.- Chapter. 11.- Triclustering in Big Data Setting.