Berry / Kogan Text Mining

Applications and Theory
1. Auflage 2010
ISBN: 978-0-470-68965-3
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 222 Seiten, E-Book

Text Mining: Applications and Theory presents thestate-of-the-art algorithms for text mining from both the academicand industrial perspectives. The contributors span severalcountries and scientific domains: universities, industrialcorporations, and government laboratories, and demonstrate the useof techniques from machine learning, knowledge discovery, naturallanguage processing and information retrieval to designcomputational models for automated text analysis and mining.
This volume demonstrates how advancements in the fields ofapplied mathematics, computer science, machine learning, andnatural language processing can collectively capture, classify, andinterpret words and their contexts. As suggested in thepreface, text mining is needed when "words are notenough."
This book:
* Provides state-of-the-art algorithms and techniques forcritical tasks in text mining applications, such as clustering,classification, anomaly and trend detection, and streamanalysis.
* Presents a survey of text visualization techniques and looks atthe multilingual text classification problem.
* Discusses the issue of cybercrime associated withchatrooms.
* Features advances in visual analytics and machine learningalong with illustrative examples.
* Is accompanied by a supporting website featuring datasets.
Applied mathematicians, statisticians, practitioners andstudents in computer science, bioinformatics and engineering willfind this book extremely useful.

Autoren/Hrsg.

Berry, Michael W

Kogan, Jacob

Inhaltsverzeichnis

List of Contributors.
Preface.
PART I TEXT EXTRACTION, CLASSIFICATION, ANDCLUSTERING.
1 Automatic keyword extraction from individualdocuments.
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 documentclustering.
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 usingmachine-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 emailclassification 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 typealgorithms.
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 andcyberbullying.
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.