Infominer: mining surprising periodic patterns Export

@inproceedings{citeulike:348652, abstract = {In this paper, we focus on mining surprising periodic patterns in a sequence of events. In many applications, e.g., computational biology, an infrequent pattern is still considered very significant if its actual occurrence frequency exceeds the prior expectation by a large margin. The traditional metric, such as support , is not necessarily the ideal model to measure this kind of surprising patterns because it treats all patterns equally in the sense that every occurrence carries the same weight towards the assessment of the significance of a pattern regardless of the probability of occurrence. A more suitable measurement, information , is introduced to naturally value the degree of surprise of each occurrence of a pattern as a continuous and monotonically decreasing function of its probability of occurrence. This would allow patterns with vastly different occurrence probabilities to be handled seamlessly. As the accumulated degree of surprise of all repetitions of a pattern, the concept of information gain is proposed to measure the overall degree of surprise of the pattern within a data sequence. The bounded information gain property is identified to tackle the predicament caused by the violation of the downward closure property by the information gain measure and in turn provides an efficient solution to this problem. Empirical tests demonstrate the efficiency and the usefulness of the proposed model.}, address = {New York, NY, USA}, author = {Yang, Jiong and Wang, Wei and Yu, Philip S.}, booktitle = {KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining}, citeulike-article-id = {348652}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=502571}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/502512.502571}, doi = {10.1145/502512.502571}, isbn = {1-58113-391-X}, keywords = {sequential\_pattern}, location = {San Francisco, California}, pages = {395--400}, posted-at = {2009-10-13 12:36:01}, priority = {2}, publisher = {ACM}, title = {Infominer: mining surprising periodic patterns}, url = {http://dx.doi.org/10.1145/502512.502571}, year = {2001} }

TY - CONF ID - citeulike:348652 L3 - citeulike-article-id:348652 N2 - In this paper, we focus on mining surprising periodic patterns in a sequence of events. In many applications, e.g., computational biology, an infrequent pattern is still considered very significant if its actual occurrence frequency exceeds the prior expectation by a large margin. The traditional metric, such as support , is not necessarily the ideal model to measure this kind of surprising patterns because it treats all patterns equally in the sense that every occurrence carries the same weight towards the assessment of the significance of a pattern regardless of the probability of occurrence. A more suitable measurement, information , is introduced to naturally value the degree of surprise of each occurrence of a pattern as a continuous and monotonically decreasing function of its probability of occurrence. This would allow patterns with vastly different occurrence probabilities to be handled seamlessly. As the accumulated degree of surprise of all repetitions of a pattern, the concept of information gain is proposed to measure the overall degree of surprise of the pattern within a data sequence. The bounded information gain property is identified to tackle the predicament caused by the violation of the downward closure property by the information gain measure and in turn provides an efficient solution to this problem. Empirical tests demonstrate the efficiency and the usefulness of the proposed model. CY - San Francisco, California SN - 1-58113-391-X AD - New York, NY, USA EP - 400 TI - Infominer: mining surprising periodic patterns PB - ACM T2 - KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining SP - 395 KW - sequential_pattern AU - Yang, Jiong AU - Wang, Wei AU - Yu, Philip PY - 2001/// UR - http://dx.doi.org/10.1145/502512.502571 ER -