Mining patterns in long sequential data with noise
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Mining for weak periodic signals in time series databases
Intelligent Data Analysis
Mining partial periodic correlations in time series
Knowledge and Information Systems
Mining Musical Patterns: Identification of Transposed Motives
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Mining periodic behaviors for moving objects
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining transposed motifs in music
Journal of Intelligent Information Systems
Mining periodic behaviors of object movements for animal and biological sustainability studies
Data Mining and Knowledge Discovery
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Discovery of periodic patterns in time series data has become an active research area with many applications. These patterns can be hierarchical in nature, where higher level pattern may consist of repetitions of lower level patterns.Unfortunately, the presence of noise m y prevent these higher level patterns from being recognized in the sense that two portions (of data sequence) that support the same (high level) pattern may have different layouts of occurrences of basic symbols. There may not exist any common representation in terms of raw symbol combinations; and hence such (high level) pattern may not be expressed by any previous model (defined on raw symbols or symbol combinations) and would not be properly recognized by any existing method. In this paper, we propose novel model, namely meta-pattern, to capture these high level patterns. As more flexible model, the number of potential meta-patterns could be very large. A substantial difficulty lies on how to identify the proper pattern candidates. However, the well-known Apriori property is not able to provide sufficient pruning power. A new property, namely component location property, is identified and used to conduct the candidate generation so that an efficient computation-based mining algorithm can be developed. Last but not least, we apply our algorithm to some real and synthetic sequences and some interesting patterns are discovered.