Coastal Geomorphology : An Introduction

by Editor: Michael W. Berry; Editor: Jacob Kogan (University of Maryland Baltimore County)

• ISBN13: 9780470689653
• ISBN10: 047068965X
• Format: eBook
• Copyright: 2010-02-01
• Publisher: Wiley

Summary

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.

Table of Contents

List of Contributors

Preface

Text Extraction, Classification, and Clustering

Automatic keyword extraction from individual documents

Introduction

Rapid automatic keyword extraction

Benchmark evaluation

Stoplist generation

Evaluation on news articles

Summary

Acknowledgements

Algebraic techniques for multilingual document clustering

Introduction

Background

Experimental setup

Multilingual LSA

Tucker1 method

PARAFAC2 method

LSA with term alignments

Latent morpho-semantic analysis (LMSA)

LMSA with term alignments

Discussion of results and techniques

Acknowledgements

Content-based spam email classification using machine-learning algorithms

Introduction

Machine-learning algorithms

Data preprocessing

Evaluation of email classification

Experiments

Characteristics of classifiers

Concluding remarks

Acknowledgements

Utilizing nonnegative matrix factorization for email classification problems

Introduction

Background

NMF initialization based on feature ranking

NMF-based classification methods

Conclusions

Acknowledgements

Constrained clustering with k -means type algorithms

Introduction

Notations and classical k -means

Constrained k -means with Bregman divergences

Constrained smoka type clustering

Constrained spherical k -means

Numerical experiments

Conclusion

Anomaly and Trend Detection

Survey of text visualization techniques

Visualization in text analysis

Tag clouds

Authorship and change tracking

Data exploration and the search for novel patterns

Sentiment tracking

Visual analytics and FutureLens

Scenario discovery

Earlier prototype

Features of FutureLens

Scenario discovery example: bioterrorism

Scenario discovery example: drug trafficking

Future work

Adaptive threshold setting for novelty mining

Introduction

Adaptive threshold setting in novelty mining

Experimental study

Conclusion

Text mining and cybercrime

Introduction

Current research in Internet predation and cyberbullying

Commercial software for monitoring chat

Conclusions and future directions

Acknowledgements

Text Streams

Events and trends in text streams

Introduction

Text streams

Feature extraction and data reduction

Event detection

Trend detection

Event and trend descriptions

Discussion

Summary

Acknowledgements

Embedding semantics in LDA topic models

Introduction

Background

Latent Dirichlet allocation

Embedding external semantics from Wikipedia

Data-driven semantic embedding

Related work

Conclusion and future work

References

Index

Table of Contents provided by Publisher. All Rights Reserved.