Institutional Repository
| extreme maximal weighted frequent itemset mining for cognitive frequency decision making | |
| Ji Pan-Pan; Liao Ming-Xue; He Xiao-Xin; Deng Yong | |
| 2011 | |
| Conference Name | 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011 |
| Source | Proceedings of 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011 |
| Pages | 267-271 |
| Conference Date | December 24, 2011 - December 26, 2011 |
| Conference Place | Harbin, China |
| 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 |
| 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.; 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. |
| Keyword | Ad Hoc Networks Algorithms Cognitive Radio Computer Science Forestry |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16280 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation 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. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment