extreme maximal weighted frequent itemset mining for cognitive frequency decision making
文献类型：会议论文
| 作者 | Ji Pan-Pan ; Liao Ming-Xue ; He Xiao-Xin ; Deng Yong |
| 出版日期 | 2011 |
| 会议名称 | 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011 |
| 会议日期 | December 24, 2011 - December 26, 2011 |
| 会议地点 | Harbin, China |
| 关键词 | Ad hoc networks Algorithms Cognitive radio Computer science Forestry |
| 页码 | 267-271 |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 会议录 | Proceedings of 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011
 |
| 语种 | 英语 |
| ISBN号 | 9781457715846 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16280]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 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]. 见:2011 International Conference on Computer Science and Network Technology, ICCSNT 2011. Harbin, China. December 24, 2011 - December 26, 2011. |
入库方式： OAI收割
来源：软件研究所
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