Deformable Markov model templates for time-series pattern matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining on time series: an illustration using fast-food restaurant franchise data
Computational Statistics & Data Analysis
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge discovery in time series databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Finding relevant sequences in time series containing crisp, interval, and fuzzy interval data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Approximate variable-length time series motif discovery using grammar inference
Proceedings of the Tenth International Workshop on Multimedia Data Mining
A review on time series data mining
Engineering Applications of Artificial Intelligence
A disk-aware algorithm for time series motif discovery
Data Mining and Knowledge Discovery
Discovery of motifs to forecast outlier occurrence in time series
Pattern Recognition Letters
Shape-based template matching for time series data
Knowledge-Based Systems
CPMD: a matlab toolbox for change point and constrained motif discovery
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
G-SteX: greedy stem extension for free-length constrained motif discovery
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Short communication: Selective Subsequence Time Series clustering
Knowledge-Based Systems
ACM Computing Surveys (CSUR)
An efficient method for discovering motifs in large time series
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
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Finding previously unknown patterns in a time series has received much attention in recent years. Of the associated algorithms, the k-motif algorithm is one of the most effective and efficient. It is also widely used as a time series preprocessing routine for many other data mining tasks. However, the k-motif algorithm depends on the predefine of the parameter w, which is the length of the pattern. This paper introduces a novel k-motif-based algorithm that can solve the existing problem and, moreover, provide a way to generate the original patterns by summarizing the discovered motifs.