Characterization and detection of noise in clustering
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Time Series Abstraction Methods - A Survey
Informatik bewegt: Informatik 2002 - 32. Jahrestagung der Gesellschaft für Informatik e.v. (GI)
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
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
Useful clustering outcomes from meaningful time series clustering
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Dynamic data assigning assessment clustering of streaming data
Applied Soft Computing
Why does subsequence time-series clustering produce sine waves?
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Behavior pattern recognition in electric power consumption series using data mining tools
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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Although k-means clustering is often applied to time series clustering, the underlying Euclidean distance measure is very restrictive in comparison to the human perception of time series. A time series and its translated copy appear dissimilar under the Euclidean distance (because the comparison is made pointwise), whereas a human would perceive both series as similar. As the human perception is tolerant to translational effects, using the cross correlation distance would be a better choice than Euclidean distance. We show how to modify a k-means variant such that it operates correctly with the cross correlation distance. The resulting algorithm may also be used for meaningful clustering of time series subsequences, which delivers meaningless results in case of Euclidean or Pearson distance.