Infominer: mining surprising periodic patterns

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 concep...

• Type: Conference
• Year: 2001
• Where: KDD
• Authors: Jiong Yang, Wei Wang 0010, Philip S. Yu
• Keywords: Actual Occurrence Frequency, Data Mining, KDD 2001, Surprising Patterns, Surprising Periodic Patterns