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Resource Adaptive Technique for Frequent Itemset Mining in Transactional Data Streams


Chandrika.J, K.R.Ananda Kumar


Vol. 12  No. 10  pp. 87-92


Mining Frequent itemsets from transactional data streams is a very challenging task as it has to handle continuous, unbounded, and ordered sequence of data elements generated at a rapid rate in a data stream. In order to enhance the analysis of stream data it is essential to extract frequent itemsets from more recent data. For this purpose a sliding window mechanism is used. Further the usage of memory resources should be taken care of regardless of the amount of data generated in the stream. The proposed algorithm RA-FIG (Resource Adaptive Frequent Item Generation) accounts for the computational resources like memory available and dynamically adapts the rate of processing based on the available memory. Extensive experimental analysis shows that proposed algorithm is efficient in terms of resource utilization and accuracy when finding recent frequent itemsets from a data stream.


Transactional data stream, sliding window, frequent itemset, Resource adaptation, Bit sequence representation.