الفهرس 
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Abstract
The increasing prominence of data streams arising in a wide range of advanced applications has led to the study of online mining of frequent itemsets. Unlike mining static databases, mining data
streams poses many new challenges. The high complexity of the
frequent itemsets mining problem hinders the application of the
stream mining techniques. In this work, a new mining model is
presented to extract frequent itemsets frorri online data stream based
on a hi erarchal Partial order set (POset) representation of the
generated itemsets. This model is flexible enough to deal with the
whole data stream or only with the recent incoming portions. Its
structure can also cope with the expiration nature of data stream by
maintaining the expired POsets into the secondary memory. This
flexible structure also enables expanding it as a variation of the
model by using the POset structure to handle the entering transactions
as the actual occurring itemsets in an online phase then generating the
corresponding embedded itemsets in an offline phase. This model
shows a reasonable performance after a fair comparison with other
analogous successful algorithms. The main advantage of this model is
the extraction of exact frequent itemsets on the whole data stream not
only the recent portion. The second advantage is gained from its variation, which is the high speed achieved in the online phase of the