%0 Journal Article %1 uno08 %A Uno, Takeaki %A Arimura, Hiroki %D 2008 %J Advances in Knowledge Discovery and Data Mining %K dataMining faultTolerant frequentSetMining listing patternMining seminar %P 357--368 %T Ambiguous Frequent Itemset Mining and Polynomial Delay Enumeration %U http://dx.doi.org/10.1007/978-3-540-68125-0_32 %X Mining frequently appearing patterns in a database is a basic problem in recent informatics, especially in data mining. Particularly, when the input database is a collection of subsets of an itemset, called transaction, the problem is called the frequent itemsetmining problem, and it has been extensively studied. The items in a frequent itemset appear in many records simultaneously,thus they can be considered to be a cluster with respect to these records. However, in this sense, the condition that everyitem appears in each record is quite strong. We should allow for several missing items in these records. In this paper, weapproach this problem from the algorithm theory, and consider the model that can be solved efficiently and possibly valuablein practice. We introduce ambiguous frequent itemsets which allow missing items in their occurrence records. More precisely,for given thresholds θ and σ, an ambiguous frequent itemset P has a transaction set

@article{uno08, abstract = {Mining frequently appearing patterns in a database is a basic problem in recent informatics, especially in data mining. Particularly, when the input database is a collection of subsets of an itemset, called transaction, the problem is called the frequent itemsetmining problem, and it has been extensively studied. The items in a frequent itemset appear in many records simultaneously,thus they can be considered to be a cluster with respect to these records. However, in this sense, the condition that everyitem appears in each record is quite strong. We should allow for several missing items in these records. In this paper, weapproach this problem from the algorithm theory, and consider the model that can be solved efficiently and possibly valuablein practice. We introduce ambiguous frequent itemsets which allow missing items in their occurrence records. More precisely,for given thresholds θ and σ, an ambiguous frequent itemset P has a transaction set}, added-at = {2009-02-22T00:31:12.000+0100}, author = {Uno, Takeaki and Arimura, Hiroki}, biburl = {https://www.bibsonomy.org/bibtex/29e36b47741f5a1993fb0282b82d8c48a/mboley}, description = {fault-tolerant frequent set mining with polynomial delay.}, interhash = {ca76bfcdfaf1de605e6e82c2ccb1b72f}, intrahash = {9e36b47741f5a1993fb0282b82d8c48a}, journal = {Advances in Knowledge Discovery and Data Mining}, keywords = {dataMining faultTolerant frequentSetMining listing patternMining seminar}, pages = {357--368}, timestamp = {2009-03-25T17:51:12.000+0100}, title = {Ambiguous Frequent Itemset Mining and Polynomial Delay Enumeration}, url = {http://dx.doi.org/10.1007/978-3-540-68125-0_32}, year = 2008 }