Articles liés à Multidimensional Mining of Massive Text Data

Multidimensional Mining of Massive Text Data - Couverture souple

 
9783031007866: Multidimensional Mining of Massive Text Data

Synopsis

Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional--they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task.

This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making.

The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos de l?auteur

Chao Zhang is an Assistant Professor in the School of Computational Science and Engineering, Georgia Institute of Technology. His research area is data mining and machine learning. He is particularly interested in developing label-efficient and robust learning techniques, with applications in text mining and spatiotemporal data mining. Chao has published more than 40 papers in top-tier conferences and journals, such as KDD, WWW, SIGIR, VLDB, and TKDE. He is the recipient of the ECML/PKDD Best Student Paper Runner-up Award (2015), Microsoft Star of Tomorrow Excellence Award (2014), and the Chiang Chen Overseas Graduate Fellowship (2013). His developed technologies have received wide media coverage and been transferred to industrial companies. Before joining Georgia Tech, he obtained his Ph.D. in Computer Science from University of Illinois at Urbana-Champaign in 2018.Jiawei Han is the Abel Bliss Professor in the Department of Computer Science, University of Illinois at Urbana-Champaign. He has been researching into data mining, information network analysis, database systems, and data warehousing, with over 900 journal and conference publications. He has chaired or served on many program committees of international conferences in most data mining and database conferences. He also served as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data and the Director of Information Network Academic Research Center supported by U.S. Army Research Lab (2009â "2016), and is the co-Director of KnowEnG, an NIH funded Center of Excellence in Big Data Computing since 2014. He is a Fellow of ACM, a Fellow of IEEE, and received 2004 ACM SIGKDD Innovations Award, 2005 IEEE Computer Society Technical Achievement Award, and 2009 M. Wallace McDowell Award from IEEE Computer Society. His co-authored book Data Mining: Concepts and Techniques has been adopted as a popular textbook worldwide.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Acheter neuf

Afficher cet article
EUR 51,51

Autre devise

EUR 9,70 expédition depuis Allemagne vers France

Destinations, frais et délais

Autres éditions populaires du même titre

9781681735191: Multidimensional Mining of Massive Text Data

Edition présentée

ISBN 10 :  1681735199 ISBN 13 :  9781681735191
Editeur : Morgan & Claypool Publishers, 2019
Couverture souple

Résultats de recherche pour Multidimensional Mining of Massive Text Data

Image fournie par le vendeur

Zhang, Chao|Han, Jiawei
ISBN 10 : 3031007867 ISBN 13 : 9783031007866
Neuf Couverture souple
impression à la demande

Vendeur : moluna, Greven, Allemagne

Évaluation du vendeur 5 sur 5 étoiles Evaluation 5 étoiles, En savoir plus sur les évaluations des vendeurs

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applicati. N° de réf. du vendeur 608129172

Contacter le vendeur

Acheter neuf

EUR 51,51
Autre devise
Frais de port : EUR 9,70
De Allemagne vers France
Destinations, frais et délais

Quantité disponible : Plus de 20 disponibles

Ajouter au panier

Image fournie par le vendeur

Jiawei Han
ISBN 10 : 3031007867 ISBN 13 : 9783031007866
Neuf Taschenbuch

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

Évaluation du vendeur 5 sur 5 étoiles Evaluation 5 étoiles, En savoir plus sur les évaluations des vendeurs

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional-they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task.This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions (2) How does one distill knowledge from text data in a multidimensional space To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making.The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain. N° de réf. du vendeur 9783031007866

Contacter le vendeur

Acheter neuf

EUR 58,84
Autre devise
Frais de port : EUR 10,99
De Allemagne vers France
Destinations, frais et délais

Quantité disponible : 1 disponible(s)

Ajouter au panier

Image fournie par le vendeur

Jiawei Han
ISBN 10 : 3031007867 ISBN 13 : 9783031007866
Neuf Taschenbuch
impression à la demande

Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

Évaluation du vendeur 5 sur 5 étoiles Evaluation 5 étoiles, En savoir plus sur les évaluations des vendeurs

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional-they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task.This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions (2) How does one distill knowledge from text data in a multidimensional space To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making.The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain. 200 pp. Englisch. N° de réf. du vendeur 9783031007866

Contacter le vendeur

Acheter neuf

EUR 58,84
Autre devise
Frais de port : EUR 11
De Allemagne vers France
Destinations, frais et délais

Quantité disponible : 2 disponible(s)

Ajouter au panier

Image d'archives

Zhang, Chao; Han, Jiawei
Edité par Springer, 2019
ISBN 10 : 3031007867 ISBN 13 : 9783031007866
Neuf Couverture souple

Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni

Évaluation du vendeur 5 sur 5 étoiles Evaluation 5 étoiles, En savoir plus sur les évaluations des vendeurs

Etat : New. In English. N° de réf. du vendeur ria9783031007866_new

Contacter le vendeur

Acheter neuf

EUR 66,84
Autre devise
Frais de port : EUR 4,62
De Royaume-Uni vers France
Destinations, frais et délais

Quantité disponible : Plus de 20 disponibles

Ajouter au panier

Image d'archives

Zhang, Chao
Edité par Springer 2019-03, 2019
ISBN 10 : 3031007867 ISBN 13 : 9783031007866
Neuf PF

Vendeur : Chiron Media, Wallingford, Royaume-Uni

Évaluation du vendeur 5 sur 5 étoiles Evaluation 5 étoiles, En savoir plus sur les évaluations des vendeurs

PF. Etat : New. N° de réf. du vendeur 6666-IUK-9783031007866

Contacter le vendeur

Acheter neuf

EUR 61,10
Autre devise
Frais de port : EUR 10,99
De Royaume-Uni vers France
Destinations, frais et délais

Quantité disponible : 10 disponible(s)

Ajouter au panier

Image fournie par le vendeur

Jiawei Han
ISBN 10 : 3031007867 ISBN 13 : 9783031007866
Neuf Taschenbuch

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

Évaluation du vendeur 5 sur 5 étoiles Evaluation 5 étoiles, En savoir plus sur les évaluations des vendeurs

Taschenbuch. Etat : Neu. Neuware -Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional¿they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task.This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions (2) How does one distill knowledge from text data in a multidimensional space To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making.The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 200 pp. Englisch. N° de réf. du vendeur 9783031007866

Contacter le vendeur

Acheter neuf

EUR 58,84
Autre devise
Frais de port : EUR 15
De Allemagne vers France
Destinations, frais et délais

Quantité disponible : 2 disponible(s)

Ajouter au panier

Image d'archives

Zhang, Chao; Han, Jiawei
Edité par Springer, 2019
ISBN 10 : 3031007867 ISBN 13 : 9783031007866
Neuf Couverture souple

Vendeur : Books Puddle, New York, NY, Etats-Unis

Évaluation du vendeur 4 sur 5 étoiles Evaluation 4 étoiles, En savoir plus sur les évaluations des vendeurs

Etat : New. 1st edition NO-PA16APR2015-KAP. N° de réf. du vendeur 26395061300

Contacter le vendeur

Acheter neuf

EUR 76,41
Autre devise
Frais de port : EUR 7,67
De Etats-Unis vers France
Destinations, frais et délais

Quantité disponible : 4 disponible(s)

Ajouter au panier

Image d'archives

Zhang, Chao; Han, Jiawei
Edité par Springer, 2019
ISBN 10 : 3031007867 ISBN 13 : 9783031007866
Neuf Couverture souple
impression à la demande

Vendeur : Majestic Books, Hounslow, Royaume-Uni

Évaluation du vendeur 5 sur 5 étoiles Evaluation 5 étoiles, En savoir plus sur les évaluations des vendeurs

Etat : New. Print on Demand. N° de réf. du vendeur 402364395

Contacter le vendeur

Acheter neuf

EUR 77,27
Autre devise
Frais de port : EUR 10,25
De Royaume-Uni vers France
Destinations, frais et délais

Quantité disponible : 4 disponible(s)

Ajouter au panier

Image d'archives

Zhang, Chao; Han, Jiawei
Edité par Springer, 2019
ISBN 10 : 3031007867 ISBN 13 : 9783031007866
Neuf Couverture souple
impression à la demande

Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne

Évaluation du vendeur 5 sur 5 étoiles Evaluation 5 étoiles, En savoir plus sur les évaluations des vendeurs

Etat : New. PRINT ON DEMAND. N° de réf. du vendeur 18395061310

Contacter le vendeur

Acheter neuf

EUR 81,55
Autre devise
Frais de port : EUR 7,95
De Allemagne vers France
Destinations, frais et délais

Quantité disponible : 4 disponible(s)

Ajouter au panier