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Multidimensional Mining Massive Text by Chao Zhang (12 results)
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Condition: New. 1st edition NO-PA16APR2015-KAP.
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Multidimensional Mining of Massive Text Data (Synthesis Lectures on Data Mining and Knowledge Discovery)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 55.92
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In English.
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Paperback. Condition: New.
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Multidimensional Mining Of Massive Text
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
Condition: New.
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Condition: New.
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Taschenbuch. Condition: Neu. Multidimensional Mining of Massive Text Data | Chao Zhang (u. a.) | Taschenbuch | Synthesis Lectures on Data Mining and Knowledge Discovery | xiii | Englisch | 2019 | Springer | EAN 9783031007866 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Multidimensional mining of massive text data(Chinese Edition)
Language: Chinese
Published by Machinery Industry Press, 2020
ISBN 10: 7111659902 ISBN 13: 9787111659907
paperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2020-07-01 Pages: 184 Publisher: Machinery Industry Press This book is co-authored by the international data mining industry leader. UIUC Professor Han Jiawei. and his student Dr. Chao Zhang (currently an assistant professor at Georgia Institute of Technology).?Introduced the data mining technology that converts unstructured text data into multi-dimensional knowledge. and explained the principle and use method of the text cube framework developed by them.
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Condition: new. Questo è un articolo print on demand.
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Condition: New. Print on Demand.
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Condition: New. PRINT ON DEMAND.
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Multidimensional Mining of Massive Text Data
Language: English
Published by Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2019
ISBN 10: 3031007867 ISBN 13: 9783031007866
Condition: 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.
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Multidimensional Mining of Massive Text Data
Language: English
Published by Springer, Palgrave Macmillan Mär 2019, 2019
ISBN 10: 3031007867 ISBN 13: 9783031007866
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. 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 KG, Sachsenplatz 4-6, 1201 Wien 200 pp. Englisch.
