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An effective projection-reduction algorithm for mining long frequents

文献类型：会议论文

作者Wang XF(王晓峰); Wang TR(王天然); Zhao Y(赵越)
出版日期2002
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
关键词top-down data mining frequent item associate rules reduction projection
页码515-519
中文摘要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.
收录类别EI ; CPCI（ISTP）
产权排序1
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
语种英语
ISBN号0-7803-7508-4
WOS记录号WOS:000181396300113
源URL[http://ir.sia.cn/handle/173321/8212]  
专题沈阳自动化研究所_工业信息学研究室_先进制造技术研究室
推荐引用方式
GB/T 7714		 Wang XF,Wang TR,Zhao Y. An effective projection-reduction algorithm for mining long frequents[C]. 见:International Conference on Machine Learning and Cybernetics. BEIJING, China. November 4-5, 2002.

入库方式： OAI收割

来源：沈阳自动化研究所

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