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Title:
extreme maximal weighted frequent itemset mining for cognitive frequency decision making
Author: Ji Pan-Pan ; Liao Ming-Xue ; He Xiao-Xin ; Deng Yong
Source: Proceedings of 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011
Conference Name: 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011
Conference Date: December 24, 2011 - December 26, 2011
Issued Date: 2011
Conference Place: Harbin, China
Keyword: Ad hoc networks ; Algorithms ; Cognitive radio ; Computer science ; Forestry
Indexed Type: EI
ISBN: 9781457715846
Department: (1) Graduate University Chinese Academy of Science Beijing China; (2) Institute of Software Chinese Academy of Science Beijing China
Abstract: 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.
English Abstract: 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.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16280
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Ji Pan-Pan,Liao Ming-Xue,He Xiao-Xin,et al. extreme maximal weighted frequent itemset mining for cognitive frequency decision making[C]. 见:2011 International Conference on Computer Science and Network Technology, ICCSNT 2011. Harbin, China. December 24, 2011 - December 26, 2011.
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