A novel frequent item set mining algorithm, namely Horizontal parallel-Apriori (HP-Apriori), is proposed that divides database both horizontally and vertically with partitioning mining process into four sub-processes so that all four tasks are performed in parallel way. In addition, the HPApriori tries to speed up the mining process by an index file that is generated in the first step of algorithm. The proposed algorithm has been compared with Count Distribution (CD) in terms of execution time and speedup criteria on the four real datasets. Experimental results demonstrated that the HPApriori outperforms over CD in terms of minimizing execution time and maximizing speedup in high scalability. We deal with the problem of detecting frequent items in a stream under the constraint that items are weighted, and recent items must be weighted more than older ones. This kind of problem naturally arises in a wide class of applications in which recent data is considered more useful and valuable with regard to older, stale data. The weight assigned to an item is, therefore, a function of its arrival timestamp.