Khai thác tập phổ biến từ dữ liệu giao dịch với nhiều ngưỡng phổ biến tối thiểu trên bộ xử lý đa nhân

Abstract

Association rule mining, one of the most important and well-researched techniques of data mining. Mining frequent itemsets are one of the most fundamental problems and most time-consuming in association rule mining. Most of the algorithms in literature used to find frequent itemsets satisfying single minimum support threshold. In practice, frequentcy of each item reflects the nature and role of items in transactional databases. This paper proposes an efficient mining parallel algorithm for frequent itemsets with multiple minimum support thresholds (a different minimum item support for each item) on Multiple-core Processors. Proposed algorithm easily extends on distributed computing systems as Hadoop, Spark. Finally, result experiments presented on both synthetic and real-life datasets show the better proposed algorithm than the existing algorithms.