extreme maximal weighted frequent itemset mining for cognitive frequency decision making

ISCAS OpenIR

	extreme maximal weighted frequent itemset mining for cognitive frequency decision making
	Ji Pan-Pan; Liao Ming-Xue; He Xiao-Xin; Deng Yong
	2011
会议名称	2011 International Conference on Computer Science and Network Technology, ICCSNT 2011
会议录名称	Proceedings of 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011
页码	267-271
会议日期	December 24, 2011 - December 26, 2011
会议地点	Harbin, China
收录类别	EI
ISBN	9781457715846
部门归属	(1) Graduate University Chinese Academy of Science Beijing China; (2) Institute of Software Chinese Academy of Science Beijing China
摘要	Cognitive Frequency Decision Making (CFDM) is a new application in cognitive radio ad hoc network with limited communication capability, and once solved by our algorithm Extreme Maximal Biclique Searcher (EMBS). In this paper, we extend the CFDM from one subnet to the whole network, and propose Common Frequency Searcher (CFS) to find the solution. CFS uses the result of a novel algorithm Maximal Weighted Frequent Itemset Mining (MWFIM) which is mainly discussed in this paper and also proposed by us to mine all maximal weighted frequent itemsets from transaction database of weighted items. We solve the extended CFDM problem by using the weight of item in a new fashion in which weight is independent of support and by traveling weighted itemset enumeration tree in a depth-first manner. When visiting nodes of the tree, we use two pruning conditions to speed up traveling and reduce computational time. Experimental results show that our algorithm can satisfy the CFDM application in real world at most times. © 2011 IEEE.; Cognitive Frequency Decision Making (CFDM) is a new application in cognitive radio ad hoc network with limited communication capability, and once solved by our algorithm Extreme Maximal Biclique Searcher (EMBS). In this paper, we extend the CFDM from one subnet to the whole network, and propose Common Frequency Searcher (CFS) to find the solution. CFS uses the result of a novel algorithm Maximal Weighted Frequent Itemset Mining (MWFIM) which is mainly discussed in this paper and also proposed by us to mine all maximal weighted frequent itemsets from transaction database of weighted items. We solve the extended CFDM problem by using the weight of item in a new fashion in which weight is independent of support and by traveling weighted itemset enumeration tree in a depth-first manner. When visiting nodes of the tree, we use two pruning conditions to speed up traveling and reduce computational time. Experimental results show that our algorithm can satisfy the CFDM application in real world at most times. © 2011 IEEE.
关键词	Ad Hoc Networks Algorithms Cognitive Radio Computer Science Forestry
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16280
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Ji Pan-Pan,Liao Ming-Xue,He Xiao-Xin,et al. extreme maximal weighted frequent itemset mining for cognitive frequency decision making[C],2011:267-271.