Abstract:
In the recent year data mining is one of the most demanding sectors of computer science which basically deals with discovering frequent patterns by using methodologies,
techniques and intelligence tools from databases. As the modern technology is growing
rapidly, high volume of data with several features are generated by modern applications.
When data set’s flowing velocity is high but applications demand real time analyzing of
data depends on immediate features then situation has become more challenging. Several
researches has been made in order to assuage the challenges regarding data streams.To
find the actual patterns as though the nature of data sets is streams, frequent patterns
of those data may be huge and requires further mining. Today’s generation are very
much interest in patterns that are significant for them and not just all frequent patterns. Already many researchers work with this topic but they are not enough sufficient.
In this thesis we proposing a novel tree based approach, CPTSW-growth which is able
to capture the uncertain data streams depending on the importance of applications and
only produces significant patterns.
Description:
This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering of East West University, Dhaka, Bangladesh.
