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Srikant}, journal = {Foundations and Trends in Networking}, number = {3}, pages = {271-379}, url = {http://dblp.uni-trier.de/db/journals/ftnet/ftnet2.html#ShakkottaiS07}, volume = {2}, year = {2007}, biburl = {http://www.bibsonomy.org/bibtex/2bfbfab4592e007604b46f40a34e4b1c6/dblp}, description = {dblp}, ee = {http://dx.doi.org/10.1561/1300000007}, date = {2008-05-21}, keywords = {dblp } } @inproceedings{agrawal95mining, title = {Mining sequential patterns}, address = {Taipei, Taiwan}, author = {Rakesh Agrawal and Ramakrishnan Srikant}, booktitle = {Eleventh International Conference on Data Engineering}, editor = {Philip S. Yu and Arbee S. P. Chen}, pages = {3--14}, publisher = {IEEE Computer Society Press}, url = {citeseer.ist.psu.edu/agrawal95mining.html}, year = {1995}, biburl = {http://www.bibsonomy.org/bibtex/20a7fa171a0ea7198f21a3ef16c922fb7/dzibold}, description = {One of the most cited papers on Mining Sequential Patterns One of the approaches used by Gruhl. Two efficient algorithms for key words extraction. Pseudo-Code of algorithms}, abstract = { We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empirically evaluate their performance using synthetic data. Two of the proposed algorithms, AprioriSome and AprioriAll, have comparable performance, albeit AprioriSome performs a little better when the}, keywords = {idiom keywords pattern_mining similarity } }