| Book Description | 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. |
| About the Author | Michael W. Berry, Professor and Associate Department Head, Department of Electrical Engineering and Computer Science, University of Tennessee.
Michael is on the Editorial board of Computing in Science and Engineering and Statistical Analysis and Data Mining Journals.
Jacob Kogan, Department of Mathematics and Statistics, University of Maryland Baltimore County, USA. |
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