Pil Sun Choi , Hwan Kim , Dae In Kim , Bu Hyun Hwang

KIPS Transactions on Software and Data Engineering, Vol. 2, No. 2, pp. 137-144, Feb. 2013
10.3745/KTSDE.2013.2.2.137,   PDF Download:

Abstract

A sequential pattern mining is finding out frequent patterns from the data set in time order. In this field, a dynamic weighted sequential pattern mining is applied to a computing environment that changes depending on the time and it can be utilized in a variety of environments applying changes of dynamic weight. In this paper, we propose a new sequence data mining method to explore the stream data by applying the dynamic weight. This method reduces the candidate patterns that must be navigated by using the dynamic weight according to the relative time sequence, and it can find out frequent sequence patterns quickly as the data input and output using a hash structure. Using this method reduces the memory usage and processing time more than applying the existing methods. We show the importance of dynamic weighted mining through the comparison of different weighting sequential pattern mining techniques.

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