Mining Closed Regular Patterns in Data Streams

Mining regular patternsin data streamsis anemerging research area and alsoachallenging problem inpresent days because in Data streams new datacomescontinuously with varying rates. Closed item setmining gained lot of implication in data mining research from conventional mining methods.So in thispaper we propose a narrative approach called CRPDS (Closed RegularPatternsin Data Streams) withvertical data format using sliding window model. To our knowledge no method has been proposed to mineclosed regular patternsin data streams. As the stream flows our CRPDS-method mines closed regularitemsets based on regularity threshold and user given support count. The experimental results show thatthe proposed method is efficient and scalable in terms of memory and time