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Text mining : applications and theory

Text mining : applications and theory (15회 대출)

자료유형
단행본
개인저자
Berry, Michael W. Kogan, Jacob, 1954-.
서명 / 저자사항
Text mining : applications and theory / [editors], Michael W. Berry, Jacob Kogan.
발행사항
Chichester, U.K. :   Wiley,   2010.  
형태사항
xiv, 207 p. : ill. ; 24 cm.
ISBN
9780470749821 (hbk.) 0470749822 (hbk.)
서지주기
Includes bibliographical references and index.
일반주제명
Data mining -- Congresses. Natural language processing (Computer science) -- Congresses.
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040 ▼a DLC ▼c DLC ▼d BTCTA ▼d YDXCP ▼d BWK ▼d BWKUK ▼d SINLB ▼d UKM ▼d BWX ▼d CDX ▼d EUM ▼d DLC ▼d 211009
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090 ▼a 006.312 ▼b T355
245 0 0 ▼a Text mining : ▼b applications and theory / ▼c [editors], Michael W. Berry, Jacob Kogan.
260 ▼a Chichester, U.K. : ▼b Wiley, ▼c 2010.
300 ▼a xiv, 207 p. : ▼b ill. ; ▼c 24 cm.
504 ▼a Includes bibliographical references and index.
650 0 ▼a Data mining ▼v Congresses.
650 0 ▼a Natural language processing (Computer science) ▼v Congresses.
700 1 ▼a Berry, Michael W.
700 1 ▼a Kogan, Jacob, ▼d 1954-.
945 ▼a KLPA

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/Sci-Info(2층서고)/ 청구기호 006.312 T355 등록번호 121223438 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

책소개

Text Mining: Applications and Theory presents the state?of?the?art algorithms for text mining from both the academic and industrial perspectives. The contributors span several countries and scientific domains: universities, industrial corporations, and government laboratories, and demonstrate the use of techniques from machine learning, knowledge discovery, natural language processing and information retrieval to design computational models for automated text analysis and mining.

This volume demonstrates how advancements in the fields of applied mathematics, computer science, machine learning, and natural language processing can collectively capture, classify, and interpret words and their contexts. As suggested in the preface, text mining is needed when “words are not enough.”

This book:

* Provides state-of-the-art algorithms and techniques for critical tasks in text mining applications, such as clustering, classification, anomaly and trend detection, and stream analysis.
* Presents a survey of text visualization techniques and looks at the multilingual text classification problem.
* Discusses the issue of cybercrime associated with chatrooms.
* Features advances in visual analytics and machine learning along with illustrative examples.
* Is accompanied by a supporting website featuring datasets.

Applied mathematicians, statisticians, practitioners and students in computer science, bioinformatics and engineering will find this book extremely useful.


정보제공 : Aladin

목차

List of Contributors.

Preface.

PART I TEXT EXTRACTION, CLASSIFICATION, AND CLUSTERING.

1 Automatic keyword extraction from individual documents.

1.1 Introduction.

1.2 Rapid automatic keyword extraction.

1.3 Benchmark evaluation.

1.4 Stoplist generation.

1.5 Evaluation on news articles.

1.6 Summary.

1.7 Acknowledgements.

2 Algebraic techniques for multilingual document clustering.

2.1 Introduction.

2.2 Background.

2.3 Experimental setup.

2.4 Multilingual LSA.

2.5 Tucker1 method.

2.6 PARAFAC2 method.

2.7 LSA with term alignments.

2.8 Latent morpho-semantic analysis (LMSA).

2.9 LMSA with term alignments.

2.10 Discussion of results and techniques.

2.11 Acknowledgements.

3 Content-based spam email classification using machine-learning algorithms.

3.1 Introduction.

3.2 Machine-learning algorithms.

3.3 Data preprocessing.

3.4 Evaluation of email classification.

3.5 Experiments.

3.6 Characteristics of classifiers.

3.7 Concluding remarks.

3.8 Acknowledgements.

4 Utilizing nonnegative matrix factorization for email classification problems.

4.1 Introduction.

4.2 Background.

4.3 NMF initialization based on feature ranking.

4.4 NMF-based classification methods.

4.5 Conclusions.

4.6 Acknowledgements.

5 Constrained clustering with k-means type algorithms.

5.1 Introduction.

5.2 Notations and classical k-means.

5.3 Constrained k-means with Bregman divergences.

5.4 Constrained smoka type clustering.

5.5 Constrained spherical k-means.

5.6 Numerical experiments.

5.7 Conclusion.

PART II ANOMALY AND TREND DETECTION.

6 Survey of text visualization techniques.

6.1 Visualization in text analysis.

6.2 Tag clouds.

6.3 Authorship and change tracking.

6.4 Data exploration and the search for novel patterns.

6.5 Sentiment tracking.

6.6 Visual analytics and FutureLens.

6.7 Scenario discovery.

6.8 Earlier prototype.

6.9 Features of FutureLens.

6.10 Scenario discovery example: bioterrorism.

6.11 Scenario discovery example: drug trafficking.

6.12 Future work.

7 Adaptive threshold setting for novelty mining.

7.1 Introduction.

7.2 Adaptive threshold setting in novelty mining.

7.3 Experimental study.

7.4 Conclusion.

8 Text mining and cybercrime.

8.1 Introduction.

8.2 Current research in Internet predation and cyberbullying.

8.3 Commercial software for monitoring chat.

8.4 Conclusions and future directions.

8.5 Acknowledgements.

PART III TEXT STREAMS.

9 Events and trends in text streams.

9.1 Introduction.

9.2 Text streams.

9.3 Feature extraction and data reduction.

9.4 Event detection.

9.5 Trend detection.

9.6 Event and trend descriptions.

9.7 Discussion.

9.8 Summary.

9.9 Acknowledgements.

10 Embedding semantics in LDA topic models.

10.1 Introduction.

10.2 Background.

10.3 Latent Dirichlet allocation.

10.4 Embedding external semantics from Wikipedia.

10.5 Data-driven semantic embedding.

10.6 Related work.

10.7 Conclusion and future work.

References.

Index.


정보제공 : Aladin

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