Many data mining techniques have been proposed
for mining useful patterns in text documents. However, how to
effectively use and update discovered patterns is still an open
research issue, especially in the domain of text mining. Since
most existing text mining methods adopted term-based
approaches, they all suffer from the problems of polysemy and
synonymy. Over the years, people have often held the hypothesis
that pattern (or phrase)-based approaches should perform better
than the term-based ones, but many experiments do not support
this hypothesis. In This approach we used an innovative and
effective pattern discovery technique which includes the
processes of pattern deploying and pattern evolving, to improve
the effectiveness of using and updating discovered patterns for
finding relevant and interesting information.
Pragati Dubey : Completed MCA from Ramdeo Baba College of
Engineering & Management, Nagpur, Maharashtra, India.
Pursuing MTech 4th sem from Rajiv Gandhi College of
Engineering & Research, Nagpur, Maharashtra, India. Her
research interests are in the area of the Text mining, Database.
Prashant Dahiwale : Pursuing PhD, Pass out MTech from VNIT,
Nagpur, Maharashtra, India. His research interests are in the area
of the Web Crawler.
Text Mining
Pattern Mining
Pattern Taxonomy
Pattern Evolution
Many data mining techniques have been proposed in the
last decade. These techniques include association rule
mining, frequent itemset mining, sequential pattern
mining, maximum pattern mining, and closed pattern
mining. However, using these discovered knowledge (or
patterns) in the field of text mining is difficult and
ineffective.
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