Hierarchical clustering of time series data with parametric derivative dynamic time warping

Łuczak, Maciej

doi:10.1016/j.eswa.2016.06.012

Cited by 78 publications

(39 citation statements)

References 36 publications

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Section: The Variable Weight Information‐based Multi‐block Pca Methodsmentioning

confidence: 99%

“…Hierarchical clustering has been an efficient clustering method in different fields including computer science and technology, mathematics, and communication engineering. [31,32] Given a dataset with n elements, hierarchical clustering starts with treating each element as a separate cluster, and then performs an iteration procedure to combine two clusters into a higherlevel cluster. In this paper, we carry out the component block division by a sequence hierarchical clustering (SHC) algorithm, which operates the hierarchical clustering by following the component order in the PCA model.…”

Section: Sequence Hierarchical Clustering Based Component Block Divisionmentioning

confidence: 99%

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Multi‐block principal component analysis based on variable weight information and its application to multivariate process monitoring

Wang

Deng

2017

Can J Chem Eng

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The traditional principal component analysis (PCA)‐based process monitoring method builds a global statistical model and omits the mining of the local variable behaviours, which may degrade the fault detection performance. Considering this problem, this paper proposes a variable weight information‐based multi‐block PCA (VWI‐MBPCA) method. Firstly, a sequence hierarchical clustering algorithm is proposed to divide the full PCA component space into several sub‐blocks, where the components sharing similar variable weight information are gathered together and then the sub‐block T2 statistic is constructed for monitoring sub‐block components. Further, the variables with small weight information on each component sub‐block are extracted to build an additional sub‐PCA model, where the trueTˆ2 statistic is developed to compensate the sub‐block T2 statistic. In order to integrate the monitoring results of each sub‐block, Bayesian inference is applied to construct an overall T2 statistic. To identify the faulty variables, a multi‐block PCA contribution plot is designed by choosing some specific blocks to highlight fault information. Finally, simulations on a numerical example and the benchmark Tennessee Eastman (TE) process are used to demonstrate the strengths of the proposed method.

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Section: The Variable Weight Information‐based Multi‐block Pca Methodsmentioning

confidence: 99%

Section: Sequence Hierarchical Clustering Based Component Block Divisionmentioning

confidence: 99%

Multi‐block principal component analysis based on variable weight information and its application to multivariate process monitoring

Wang

Deng

2017

Can J Chem Eng

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“…As the conic method is invariant to scalar multiplication, so too is it invariant to temporal "stretching" which corresponds to a conserved order of events despite irregularity in the precise timing. This problem of temporal "stretching" is referred to as dynamic time warping (DTW), and has been discussed by many previous authors, including in the use of DTW on derivative time series for classification ( [28], [29], [30]). In general, dynamic time warping algorithms give a similarity measure between time series evolving at different rates by comparing the order of events between time-series rather than on a point-by-point basis.…”

Section: Invariant Properties Promote Robust Classificationmentioning

confidence: 99%

Geometric classification of brain network dynamics via conic derivative discriminants

Singh

Braver

Ching

2017

Preprint

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Over the past decade, pattern decoding techniques have granted neuroscientists improved anatomical specificity in mapping neural representations associated with function and cognition. Dynamical patterns are of particular interest, as evidenced by the proliferation and success of frequency domain methods that reveal structured spatiotemporal rhythmic brain activity. One drawback of such approaches, however, is the need to estimate spectral power, which limits the temporal resolution of classification. We propose an alternative method that enables classification of dynamical patterns with high temporal fidelity. The key feature of the method is a conversion of time-series into their temporal derivatives. By doing so, dynamically-coded information may be revealed in terms of geometric patterns in the phase space of the derivative signal. We derive a geometric classifier for this problem which simplifies into a straightforward calculation in terms of covariances. We demonstrate the relative advantages and disadvantages of the technique with simulated data and benchmark its performance with an EEG dataset of covert spatial attention. By mapping the weights anatomically we reveal a retinotopic organization of covert spatial attention. We especially highlight the ability of the method to provide strong group-level classification performance compared to existing benchmarks, while providing information that is synergistic to classical spectral-based techniques. The robustness and sensitivity of the method to noise is also examined relative to spectral-based techniques. The proposed classification technique enables decoding of dynamic patterns with high temporal resolution, performs favorably to benchmark methods, and facilitates anatomical inference.

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“…A considerable number of techniques have been proposed in the literature for clustering, amongst which the already mentioned K-means [26] and hierarchical clustering [27,28] are probably the most widely used. One characteristic of K-means is that this algorithm is simple and easy to understand.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Machine Learning for Advanced Tolerance Monitoring of Wire Electrical Discharge Machining of Disc Turbine Fir-Tree Slots

Wang

Sánchez

Ayesta

et al. 2018

Sensors

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Manufacturing more efficient low pressure turbines has become a topic of primary importance for aerospace companies. Specifically, wire electrical discharge machining of disc turbine fir-tree slots has attracted increasing interest in recent years. However, important issues must be still addressed for optimum application of the WEDM process for fir-tree slot production. The current work presents a novel approach for tolerance monitoring based on unsupervised machine learning methods using distribution of ionization time as a variable. The need for time-consuming experiments to set-up threshold values of the monitoring signal is avoided by using K-means and hierarchical clustering. The developments have been tested in the WEDM of a generic fir-tree slot under industrial conditions. Results show that 100% of the zones classified into Clusters 1 and 2 are related to short-circuit situations. Further, 100% of the zones classified in Clusters 3 and 5 lie within the tolerance band of ±15 μm. Finally, the 9 regions classified in Cluster 4 correspond to situations in which the wire is moving too far away from the part surface. These results are strongly in accord with tolerance distribution as measured by a coordinate measuring machine.

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Hierarchical clustering of time series data with parametric derivative dynamic time warping

Cited by 78 publications

References 36 publications

Multi‐block principal component analysis based on variable weight information and its application to multivariate process monitoring

Multi‐block principal component analysis based on variable weight information and its application to multivariate process monitoring

Geometric classification of brain network dynamics via conic derivative discriminants

Unsupervised Machine Learning for Advanced Tolerance Monitoring of Wire Electrical Discharge Machining of Disc Turbine Fir-Tree Slots

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