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Applying Data Mining Techniques in Text Analysis
 

Summary: Applying Data Mining Techniques
in Text Analysis
Helena Ahonen Oskari Heinonen
Mika Klemettinen A. Inkeri Verkamo
fhahonen,oheinone,mklemett,verkamog@cs.helsinki.fi
University of Helsinki, Department of Computer Science
P.O. Box 26, FIN--00014 University of Helsinki, Finland
Abstract
A number of recent data mining techniques have been targeted especially for the
analysis of sequential data. Traditional examples of sequential data involve telecom­
munication alarms, Www log files, user action registration for Hci studies, or any
other series of events consisting of an event type and a time of occurrence.
Text can also be seen as sequential data, in many respects similar to the data
collected by sensors, or other observation systems. Traditionally, texts have been
analysed using various information retrieval related methods, such as full­text ana­
lysis, and natural language processing. However, only few examples of data mining
in text, particularly in full text, are available.
In this paper we show that general data mining methods are applicable to text
analysis tasks under certain conditions. Moreover, we present a general framework
for text mining. The framework follows the general Kdd process, thus containing

  

Source: Ahonen, Helena - Department of Computer Science, University of Helsinki

 

Collections: Computer Technologies and Information Sciences