MALM: a framework for mining sequence database at multiple abstraction levels
Proceedings of the seventh international conference on Information and knowledge management
Nonlinear time series analysis
Nonlinear time series analysis
Identifying distinctive subsequences in multivariate time series by clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Classification Rules + Time = Temporal Rules
ICCS '02 Proceedings of the International Conference on Computational Science-Part I
Indexing and Mining of the Local Patterns in Sequence Database
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
A Motion Recognition Method by Using Primitive Motions
VDB 5 Proceedings of the Fifth Working Conference on Visual Database Systems: Advances in Visual Information Management
Maintaining variance and k-medians over data stream windows
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
IEEE Transactions on Knowledge and Data Engineering
Useful clustering outcomes from meaningful time series clustering
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Establishing relationships among patterns in stock market data
Data & Knowledge Engineering
Data mining of vector–item patterns using neighborhood histograms
Knowledge and Information Systems
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Sequential time series clustering is a technique used to extract important features from time series data. The method can be shown to be the process of clustering in the delay-vector space formalism used in the Dynamical Systems literature. Recently, the startling claim was made that sequential time series clustering is meaningless. This has important consequences for a significant amount of work in the literature, since such a claim invalidates these work’s contribution. In this paper, we show that sequential time series clustering is not meaningless, and that the problem highlighted in these works stem from their use of the Euclidean distance metric as the distance measure in the delay-vector space. As a solution, we consider quite a general class of time series, and propose a regime based on two types of similarity that can exist between delay vectors, giving rise naturally to an alternative distance measure to Euclidean distance in the delay-vector space. We show that, using this alternative distance measure, sequential time series clustering can indeed be meaningful. We repeat a key experiment in the work on which the “meaningless” claim was based, and show that our method leads to a successful clustering outcome.