{"product_id":"text-mining-9780470749821","title":"Text Mining","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eText Mining: Applications and Theory presents the state-of-the-art algorithms for text mining from both the academic and industrial perspectives.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"It is extremely useful for practitioners and students in computer science, natural language processing, bioinformatics and engineering who wish to use text mining techniques.\" (Journal of Information Retrieval, 1 April 2011)\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eList of Contributors.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003ePreface.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I TEXT EXTRACTION, CLASSIFICATION, AND CLUSTERING.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Automatic keyword extraction from individual documents.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction.\u003c\/p\u003e \u003cp\u003e1.2 Rapid automatic keyword extraction.\u003c\/p\u003e \u003cp\u003e1.3 Benchmark evaluation.\u003c\/p\u003e \u003cp\u003e1.4 Stoplist generation.\u003c\/p\u003e \u003cp\u003e1.5 Evaluation on news articles.\u003c\/p\u003e \u003cp\u003e1.6 Summary.\u003c\/p\u003e \u003cp\u003e1.7 Acknowledgements.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Algebraic techniques for multilingual document clustering.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction.\u003c\/p\u003e \u003cp\u003e2.2 Background.\u003c\/p\u003e \u003cp\u003e2.3 Experimental setup.\u003c\/p\u003e \u003cp\u003e2.4 Multilingual LSA.\u003c\/p\u003e \u003cp\u003e2.5 Tucker1 method.\u003c\/p\u003e \u003cp\u003e2.6 PARAFAC2 method.\u003c\/p\u003e \u003cp\u003e2.7 LSA with term alignments.\u003c\/p\u003e \u003cp\u003e2.8 Latent morpho-semantic analysis (LMSA).\u003c\/p\u003e \u003cp\u003e2.9 LMSA with term alignments.\u003c\/p\u003e \u003cp\u003e2.10 Discussion of results and techniques.\u003c\/p\u003e \u003cp\u003e2.11 Acknowledgements.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Content-based spam email classification using machine-learning algorithms.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction.\u003c\/p\u003e \u003cp\u003e3.2 Machine-learning algorithms.\u003c\/p\u003e \u003cp\u003e3.3 Data preprocessing.\u003c\/p\u003e \u003cp\u003e3.4 Evaluation of email classification.\u003c\/p\u003e \u003cp\u003e3.5 Experiments.\u003c\/p\u003e \u003cp\u003e3.6 Characteristics of classifiers.\u003c\/p\u003e \u003cp\u003e3.7 Concluding remarks.\u003c\/p\u003e \u003cp\u003e3.8 Acknowledgements.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Utilizing nonnegative matrix factorization for email classification problems.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction.\u003c\/p\u003e \u003cp\u003e4.2 Background.\u003c\/p\u003e \u003cp\u003e4.3 NMF initialization based on feature ranking.\u003c\/p\u003e \u003cp\u003e4.4 NMF-based classification methods.\u003c\/p\u003e \u003cp\u003e4.5 Conclusions.\u003c\/p\u003e \u003cp\u003e4.6 Acknowledgements.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Constrained clustering with \u003ci\u003ek\u003c\/i\u003e-means type algorithms.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction.\u003c\/p\u003e \u003cp\u003e5.2 Notations and classical \u003ci\u003ek\u003c\/i\u003e-means.\u003c\/p\u003e \u003cp\u003e5.3 Constrained \u003ci\u003ek\u003c\/i\u003e-means with Bregman divergences.\u003c\/p\u003e \u003cp\u003e5.4 Constrained smoka type clustering.\u003c\/p\u003e \u003cp\u003e5.5 Constrained spherical \u003ci\u003ek\u003c\/i\u003e-means.\u003c\/p\u003e \u003cp\u003e5.6 Numerical experiments.\u003c\/p\u003e \u003cp\u003e5.7 Conclusion.\u003cbr\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II ANOMALY AND TREND DETECTION.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Survey of text visualization techniques.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Visualization in text analysis.\u003c\/p\u003e \u003cp\u003e6.2 Tag clouds.\u003c\/p\u003e \u003cp\u003e6.3 Authorship and change tracking.\u003c\/p\u003e \u003cp\u003e6.4 Data exploration and the search for novel patterns.\u003c\/p\u003e \u003cp\u003e6.5 Sentiment tracking.\u003c\/p\u003e \u003cp\u003e6.6 Visual analytics and FutureLens.\u003c\/p\u003e \u003cp\u003e6.7 Scenario discovery.\u003c\/p\u003e \u003cp\u003e6.8 Earlier prototype.\u003c\/p\u003e \u003cp\u003e6.9 Features of FutureLens.\u003c\/p\u003e \u003cp\u003e6.10 Scenario discovery example: bioterrorism.\u003c\/p\u003e \u003cp\u003e6.11 Scenario discovery example: drug trafficking.\u003c\/p\u003e \u003cp\u003e6.12 Future work.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Adaptive threshold setting for novelty mining.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 Adaptive threshold setting in novelty mining.\u003c\/p\u003e \u003cp\u003e7.3 Experimental study.\u003c\/p\u003e \u003cp\u003e7.4 Conclusion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Text mining and cybercrime.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 Current research in Internet predation and cyberbullying.\u003c\/p\u003e \u003cp\u003e8.3 Commercial software for monitoring chat.\u003c\/p\u003e \u003cp\u003e8.4 Conclusions and future directions.\u003c\/p\u003e \u003cp\u003e8.5 Acknowledgements.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III TEXT STREAMS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Events and trends in text streams.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction.\u003c\/p\u003e \u003cp\u003e9.2 Text streams.\u003c\/p\u003e \u003cp\u003e9.3 Feature extraction and data reduction.\u003c\/p\u003e \u003cp\u003e9.4 Event detection.\u003c\/p\u003e \u003cp\u003e9.5 Trend detection.\u003c\/p\u003e \u003cp\u003e9.6 Event and trend descriptions.\u003c\/p\u003e \u003cp\u003e9.7 Discussion.\u003c\/p\u003e \u003cp\u003e9.8 Summary.\u003c\/p\u003e \u003cp\u003e9.9 Acknowledgements.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Embedding semantics in LDA topic models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction.\u003c\/p\u003e \u003cp\u003e10.2 Background.\u003c\/p\u003e \u003cp\u003e10.3 Latent Dirichlet allocation.\u003c\/p\u003e \u003cp\u003e10.4 Embedding external semantics from Wikipedia.\u003c\/p\u003e \u003cp\u003e10.5 Data-driven semantic embedding.\u003c\/p\u003e \u003cp\u003e10.6 Related work.\u003c\/p\u003e \u003cp\u003e10.7 Conclusion and future work.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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