|
Boukerche A. Handbook of Algorithms for WirelessNetworking and Mobile Computing. Chapman Hall/CRC, 2005.[2]Boukerche A and Samarah S. A novel algorithm for miningassociation rules in wireless Ad hoc sensor networks. IEEETransactions on Parallel and Distributed Systems, 2008,19(7): 143-160.[3]Laxman S. Stream prediction using a generative model basedon frequent episodes in event. Knowledge Discovery and DataMining Conference, Las Vegas, US. Aug. 24-27, 2008:101-110.[4]Laxman S, Sastry P S, and Unnikrishnan K P. Discoveringfrequent episodes and learning Hidden Markov Models: Aformal connection[J].IEEE Transactions on Knowledge andData Engineering.2005, 17(11):1505-1517[5]Chudova D and Smyth P. Pattern discovery in sequencesunder a Markovian assumption. Knowledge Discovery andData Mining Conference, Alberta, Canada, July 17-19 2002:109-118.[6]Alon J, Sclaroff S, Kollios G, and Pavlovic V. Discoveringclusters in motion time series data. Computer Vision andPattern Recognition Conference, Wisconsin, U S, June 2003:I-375-I-381.[7]Wang X and Kabn A. A dynamic bibliometric model foridentifying online communities. Journal of Data MiningKnowledge Discovery, 2008, 10(3): 42-68.[8]Kiernan J and Terzi E. Constructing comprehensivesummaries of large event sequences. Knowledge Discoveryand Data Mining Conference, Las Vegas, U.S. Aug. 24-27,2008: 131-140.[9]Pei J, Han J, Pinto H, Chen Q, Dayal U, and Hsu M C.PrefixSpan: Mining sequential patterns efficiently byprefix-projected pattern growth. International Conference ofData Engineering, Heidelberg, Germany, 2001: 215-224.[10]Meger N and Rigotti C. Constraint-based mining of episoderules and optimal window sizes. 8th European Conference onPrinciples and Practice of Knowledge Discovery in Databases,Pisa, Italy, 2004: 313-324.
|
下载:
