الفهرس 
Sequential algorithm for finding frequent itemsetsOnly 14 pages are availabe for public view
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Abstract
Discovering the frequent item sets requires a lot of computation power,
memory and input/output values, which is better to be provided by
parallel processing. The implementation of data mining ideas in high
performance parallel and distributed computing environments is
becoming crucial for ensuring system scalability and interactivity as data
continues to grow in size and complexity.
The aim of this work is to present a modified parallel algorithm that
makes an enhancement of parallel buddy prima algorithm. The modified
parallel algorithm doesn’t need to load all transaction in memory. It may
be inapplicable for large data sets with many distinct items. Modified
algorithm prevent full scan of all transaction set each time we calculate
the support count of a candidate set. The algorithm is implemented, and is
compared to its predecessor algorithms. The comparisons were made on
processing time and the accuracy of each algorithm. Results of applying
the proposed algorithm show faster performance than other algorithms
without scarifying the accuracy.