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Summation of the number of outperformances of each algorithm for all datasets and all metrics compared to the mini- batch-kmeans with default parameters

Summation of the number of outperformances of each algorithm for all datasets and all metrics compared to the mini- batch-kmeans with default parameters

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Figure 1: Combination possibilities of time-series clustering categories.
Algorithms used for time-series clustering comparison
Summation of the number of outperformances of each algorithm for all...
Summation of the number of outperformances of each algorithm for all...
+7 Best metric 'metrics.mt3scm' value for each dataset and algorithm from...
Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm
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  • Sep 2022
This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such data has become more and more important due to the increase of availability in many industrial fields. Labeling, sorting or filtering highly transient...
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Context 1
... 5 shows the results for the hyperparameter search and the number of outperformances of each algorithm compared to the "MiniBatchKMeans" algorithm. Table 6 shows the same results with default parameter settings for each algorithm. We can see, that in sum and in two of the metrics our algorithm beats state-of-the-art algorithms. ...