Sliding window technique to mine regular frequent patterns in data streams using vertical format

Mining interesting patterns from various domains is essential in data mining and knowledge discovery process. Recently, frequent patterns along with regularity have good reputation in data mining research. A frequent pattern is regular frequent pattern if it occurs at less than or equal to user give...

Full description

Saved in:
Bibliographic Details
Published in2012 IEEE International Conference on Computational Intelligence and Computing Research pp. 1 - 4
Main Authors Kumar, G. Vijay, Kumari, V. Valli
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2012
Subjects
Computational modeling
Data mining
Data models
Data Streams
frequent patterns
Itemsets
Knowledge discovery
regular patterns
sliding window
Traffic control
vertical data format
Web page design
Web pages
Web sites
Online AccessGet full text
ISBN1467313424
9781467313421
DOI10.1109/ICCIC.2012.6510285

Cover

More Information
Summary:Mining interesting patterns from various domains is essential in data mining and knowledge discovery process. Recently, frequent patterns along with regularity have good reputation in data mining research. A frequent pattern is regular frequent pattern if it occurs at less than or equal to user given maximum regularity threshold. So, in this paper we are introducing a new algorithm RFPDS to mine frequent patterns that occur at regular intervals from high speed data streams with sliding window technique using vertical data format, satisfies downward closure property. Our experiment results show the outperformance of our algorithm in discovering recent regular frequent patterns from a high speed data stream.
ISBN:1467313424
9781467313421
DOI:10.1109/ICCIC.2012.6510285