Data Mining Iv
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Data Mining IV
Sixty-three papers from a December 2003 conference describe recent advances in data mining problems, encompassing both original research results and practical development experience. The goal is to develop algorithms and data structures that facilitate analysis of large amounts of data. Contributors from academia and industry cover such diverse areas as machine learning, databases, statistics, knowledge acquisitions, data visualization, and knowledge-based systems. Papers are organized in sections on data and text mining, clustering, categorization, CRM, case studies, post-processing and knowledge evaluation, genomics and bioinformatics, novel applications, and scalable algorithms and high- performance platforms. There is no subject index. The US office of WIT Press is Computational Mechanics. Annotation : 2004 Book News, Inc., Portland, OR (booknews.com).
Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications
Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. Winner of a 2012 PROSE Award in Computing and Information Sciences from the Association of American Publishers, this book presents a comprehensive how-to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities. The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. - Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible - Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com - Glossary of text mining terms provided in the appendix