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题名: An effective projection-reduction algorithm for mining long frequents
作者: Wang XF(王晓峰) ; Wang TR(王天然) ; Zhao Y(赵越)
作者部门: 先进制造技术研究室
会议名称: International Conference on Machine Learning and Cybernetics
会议日期: November 4-5, 2002
会议地点: BEIJING, China
会议主办者: IEEE, SMC, Hebei Univ, Machine Learning Ctr
会议录: 2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS
会议录出版者: IEEE
会议录出版地: NEW YORK
出版日期: 2002
页码: 515-519
收录类别: CPCI(ISTP) ; EI
ISBN号: 0-7803-7508-4
关键词: top-down ; data mining ; frequent item ; associate rules ; reduction ; projection
摘要: An effective Projection-reduction Algorithm for mining long patterns frequent is presented. A new ideal of top-down ning frequent items is adopted, and some of new conceptions such as transaction and items association information tables, key-items and reduction items, projection DB. etc. are proposed, The algorithm presented is very effective for mining long frequents, the validity of proposed algorithms is proved through analysis computing complexity, Some examples of computation are given also. The computing complexity of the algorithm has relation to the average length of items reduction, the complexity approximates to 0.5xM(3)N xO(2(S)xN'(2)) in worst of case. here, S is the average length of items reduction under min-support given by user, N' is the number of tuples in the database, N is numbers of the transaction in databases, M is the average length of item sets in databases. On the side, using heuristic information for pruning useless candidate frequent itemsets, the efficiency of algorithm is improved notably. It is very effective for mining long frequent, since S is very short for long pattern frequent, the computing complexity approach to polynomial time.
产权排序: 1
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/8212
Appears in Collections:工业信息学研究室_先进制造技术研究室_会议论文

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