سال انتشار: ۱۳۸۵
محل انتشار: دوازدهمین کنفرانس سالانه انجمن کامپیوتر ایران
تعداد صفحات: ۷
Mahmood Deypir – Department of Computer Science and Engineering, School of Engineering, Shiraz University, Shiraz, Iran.
Mohammad Hadi Sadreddini – Department of Computer Science and Engineering, School of Engineering, Shiraz University, Shiraz, Iran.
distributed association rules mining algorithms treat all types of frequent itemsets as being the same, while there are different types of itemsets in distributed databases, e.g., derivable and non-derivable. In this study a new technique is developed to reduce communication and computation by exploiting derivability of itemsets in distributed data. In this technique derivable frequent itemsets are mined without any communication and I/O costs. This approach can be utilized in every distributed association rules mining algorithm. Experimental evaluations on real-life datasets show the effectiveness of our technique in terms of communication and run time.