Incremental Mining from News Streams

Incremental Mining from News Streams

Seokkyung Chung (University of Southern California, USA)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-60566-010-3.ch157
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Abstract

With the rapid growth of the World Wide Web, Internet users are now experiencing overwhelming quantities of online information. Since manually analyzing the data becomes nearly impossible, the analysis would be performed by automatic data mining techniques to fulfill users’ information needs quickly. On most Web pages, vast amounts of useful knowledge are embedded into text. Given such large sizes of text collection, mining tools, which organize the text datasets into structured knowledge, would enhance efficient document access. This facilitates information search and, at the same time, provides an efficient framework for document repository management as the number of documents becomes extremely huge. Given that the Web has become a vehicle for the distribution of information, many news organizations are providing newswire services through the Internet. Given this popularity of the Web news services, text mining on news datasets has received significant attentions during the past few years. In particular, as several hundred news stories are published everyday at a single Web news site, triggering the whole mining process whenever a document is added to the database is computationally impractical. Therefore, efficient incremental text mining tools need to be developed.
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Main Thrust

In the following, we will explore text mining approaches that are relevant for news streams data.

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