Attribute Constrained Rules for Partially Labeled Sequence Completion Export

@article{WNM09, abstract = {Sequential pattern and rule mining have been the focus of much research, however predicting missing sets of elements within a sequence remains a challenge. Recent work in survey design suggests that if these missing elements can be inferred with a higher degree of certainty, it could greatly reduce the time burden on survey participants. To address this problem and the more general problem of missing sensor data, we introduce a new form of constrained sequential rules that use attribute presence to better capture rule confidence in sequences with missing data than previous constraint based techniques. Specifically we examine the problem of given a partially labeled sequence of sets, how well can the missing attributes be inferred. Our study shows this technique significantly improves prediction robustness when even large amounts of data are missing compared to traditional techniques.}, address = {Leipzig, Germany}, author = {Williams, Chad A. and Nelson, Peter C. and Mohammadian, Abolfazl}, citeulike-article-id = {5879437}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-03067-3_27}, citeulike-linkout-1 = {http://www.springerlink.com/content/h7726636q533q36k}, citeulike-linkout-2 = {http://www.data-mining-forum.de/index.php}, doi = {10.1007/978-3-642-03067-3_27}, editor = {Perner, Petra}, journal = {Advances in Data Mining - Applications and Theoretical Aspects}, keywords = {associative, attributes, constrained, constrained-rules, frequent-sequence-mining, mining, missing, missing-data, patterns, rule, rules, sequences, sequential, values}, month = {July}, pages = {338--352}, posted-at = {2009-10-02 19:56:21}, priority = {0}, publisher = {Springer Berlin / Heidelberg}, series = {Lecture Notes in Computer Science}, title = {Attribute Constrained Rules for Partially Labeled Sequence Completion}, url = {http://dx.doi.org/10.1007/978-3-642-03067-3_27}, volume = {5633}, year = {2009} }

TY - JOUR ID - WNM09 L3 - citeulike-article-id:5879437 N2 - Sequential pattern and rule mining have been the focus of much research, however predicting missing sets of elements within a sequence remains a challenge. Recent work in survey design suggests that if these missing elements can be inferred with a higher degree of certainty, it could greatly reduce the time burden on survey participants. To address this problem and the more general problem of missing sensor data, we introduce a new form of constrained sequential rules that use attribute presence to better capture rule confidence in sequences with missing data than previous constraint based techniques. Specifically we examine the problem of given a partially labeled sequence of sets, how well can the missing attributes be inferred. Our study shows this technique significantly improves prediction robustness when even large amounts of data are missing compared to traditional techniques. JF - Advances in Data Mining - Applications and Theoretical Aspects SE - Lecture Notes in Computer Science AD - Leipzig, Germany EP - 352 TI - Attribute Constrained Rules for Partially Labeled Sequence Completion PB - Springer Berlin / Heidelberg VL - 5633 SP - 338 KW - associative KW - attributes KW - constrained KW - constrained-rules KW - frequent-sequence-mining KW - mining KW - missing KW - missing-data KW - patterns KW - rule KW - rules KW - sequences KW - sequential KW - values AU - Williams, Chad AU - Nelson, Peter AU - Mohammadian, Abolfazl A2 - Perner, Petra PY - 2009/July// UR - http://dx.doi.org/10.1007/978-3-642-03067-3_27 ER -