The world of text mining is simultaneously a minefield and a gold mine. It is an exciting application field and an area of scientific research that is currently under rapid development. It uses techniques from well-established scientific fields (e.g. data mining, machine learning, information retrieval, natural language processing, case based reasoning, statistics and knowledge management) in an effort to help people gain insight, understand and interpret large quantities of (usually) semi-structured and unstructured data. Despite the advances made during the last few years, many issues remain umesolved. Proper co-ordination activities, dissemination of current trends and standardisation of the procedures have been identified, as key needs. There are many questions still unanswered, especially to the potential users; what is the scope of Text Mining, who uses it and for what purpose, what constitutes the leading trends in the field of Text Mining -especially in relation to IT- and whether there still remain areas to be covered.
The world of text mining is simultaneously a minefield and a gold mine. It is an exciting application field and an area of scientific research that is currently under rapid development. It uses techniques from well-established scientific fields (e.g. data mining, machine learning, information retrieval, natural language processing, case based reasoning, statistics and knowledge management) in an effort to help people gain insight, understand and interpret large quantities of (usually) semi-structured and unstructured data. Despite the advances made during the last few years, many issues remain umesolved. Proper co-ordination activities, dissemination of current trends and standardisation of the procedures have been identified, as key needs. There are many questions still unanswered, especially to the potential users; what is the scope of Text Mining, who uses it and for what purpose, what constitutes the leading trends in the field of Text Mining -especially in relation to IT- and whether there still remain areas to be covered.
This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in database systems, and presents a broad, yet in-depth overview of the field of data mining...
Social media sites generate massive volumes of natural language data that are available for social science research. But social scientists have struggled to take advantage of undefinedbig...
Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical...
Text Mining and Visualization: Case Studies Using Open-Source Tools provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R,...
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