國立虎尾科技大學 |

Multidimensional mining of massive text data /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Multidimensional mining of massive text data // Chao Zhang, Jiawei Han.
作者:	Zhang, Chao
其他作者:	Han, Jiawei,
面頁冊數:	1 PDF (xiv, pages) :illustrations. :
附註:	Part of: Synthesis digital library of engineering and computer science.
標題:	Data mining. -
電子資源:	https://ieeexplore.ieee.org/servlet/opac?bknumber=8673866
電子資源:	https://doi.org/10.2200/S00903ED1V01Y201902DMK017
ISBN:	9781681735207

Multidimensional mining of massive text data /
Zhang, Chao(Computer scientist),

Multidimensional mining of massive text data /Chao Zhang, Jiawei Han. - 1 PDF (xiv, pages) :illustrations. - Synthesis lectures on data mining and knowledge discovery,#172151-0067 ;. - Synthesis digital library of engineering and computer science..

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 169-181).

1. Introduction -- 1.1. Overview -- 1.2. Main parts -- 1.3. Technical roadmap -- 1.4. Organization

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Compendex

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.

Mode of access: World Wide Web.

ISBN: 9781681735207

Standard No.: 10.2200/S00903ED1V01Y201902DMK017doiSubjects--Topical Terms:

528622
Data mining.
Subjects--Index Terms:

text mining

LC Class. No.: QA76.9.D343 / Z536 2019eb

Dewey Class. No.: 006.312

Multidimensional mining of massive text data /
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245 1 0 $a Multidimensional mining of massive text data / $c Chao Zhang, Jiawei Han.
264 1 $a [San Rafael, California] : $b Morgan & Claypool, $c [2019]
300 $a 1 PDF (xiv, pages) : $b illustrations.
336 $a text $2 rdacontent
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490 1 $a Synthesis lectures on data mining and knowledge discovery, $x 2151-0067 ; $v #17
500 $a Part of: Synthesis digital library of engineering and computer science.
504 $a Includes bibliographical references (pages 169-181).
505 0 $a 1. Introduction -- 1.1. Overview -- 1.2. Main parts -- 1.3. Technical roadmap -- 1.4. Organization
505 8 $a part I. Cube construction algorithms. 2. Topic-level taxonomy generation -- 2.1. Overview -- 2.2. Related work -- 2.3. Preliminaries -- 2.4. Adaptive term clustering -- 2.5. Adaptive term embedding -- 2.6. Experimental evaluation -- 2.7. Summary
505 8 $a 3. Term-level taxonomy generation / Jiaming Shen -- 3.1. Overview -- 3.2. Related work -- 3.3. Problem formulation -- 3.4. The HiExpan framework -- 3.5. Experiments -- 3.6. Summary
505 8 $a 4. Weakly supervised text classification / Yu Meng -- 4.1. Overview -- 4.2. Related work -- 4.3. Preliminaries -- 4.4. Pseudo-document generation -- 4.5. Neural models with self-training -- 4.6. Experiments -- 4.7. Summary 69
505 8 $a 5. Weakly supervised hierarchical text classification / Yu Meng -- 5.1. Overview -- 5.2. Related work -- 5.3. Problem formulation -- 5.4. Pseudo-document generation -- 5.5. The hierarchical classification model -- 5.6. Experiments -- 5.7. Summary
505 8 $a part II. Cube exploitation algorithms. 6. Multidimensional summarization / Fangbo Tao -- 6.1. Introduction -- 6.2. Related work -- 6.3. Preliminaries -- 6.4. The ranking measure -- 6.5. The RepPhrase method -- 6.6. Experiments -- 6.7. Summary
505 8 $a 7. Cross-dimension prediction in cube space -- 7.1. Overview -- 7.2. Related work -- 7.3. Preliminaries -- 7.4. Semi-supervised multimodal embedding -- 7.5. Online updating of multimodal embedding -- 7.6. Experiments -- 7.7. Summary
505 8 $a 8. Event detection in cube space -- 8.1. Overview -- 8.2. Related work -- 8.3. Preliminaries -- 8.4. Candidate generation -- 8.5. Candidate classification -- 8.6. Supporting continuous event detection -- 8.7. Complexity analysis -- 8.8. Experiments -- 8.9. Summary
505 8 $a 9. Conclusions -- 9.1. Summary -- 9.2. Future work.
506 $a Abstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 $a Compendex
510 0 $a INSPEC
510 0 $a Google scholar
510 0 $a Google book search
520 3 $a 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.
530 $a Also available in print.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Title from PDF title page (viewed on April 2, 2019).
650 0 $a Data mining. $3 528622
650 0 $a Text processing (Computer science) $3 528615
653 $a text mining
653 $a multidimensional analysis
653 $a data cube
653 $a limited supervision
700 1 $a Han, Jiawei, $e author. $3 1253071
776 0 8 $i Print version: $z 9781681735214 $z 9781681735191
830 0 $a Synthesis digital library of engineering and computer science. $3 598254
830 0 $a Synthesis lectures on data mining and knowledge discovery ; $v # 16. $x 2151-0075 $3 1253064
856 4 2 $3 Abstract with links to resource $u https://ieeexplore.ieee.org/servlet/opac?bknumber=8673866
856 4 0 $3 Abstract with links to full text $u https://doi.org/10.2200/S00903ED1V01Y201902DMK017