Regional economic disparities in Europe: time-series clustering of NUTS 3 regions
, 2023 The aim of this research is to identify the regional economic disparities in the level of economic wealth and its dynamics in the NUTS 3 regions in EU28 over the period from 2000 to 2017.
López-Villuendas, Ana María +1 more
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Unsupervised Multivariate Time Series Clustering
, 2021 Clustering is widely used in unsupervised machine learning to partition a given set of data into non-overlapping groups. Many real-world applications require processing more complex multivariate time series data characterized by more than one dependent ...
Glandon, Alex +3 more
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Iterative Incremental Clustering of Time Series [PDF]
, 2004 We present a novel anytime version of partitional clustering algorithm, such as k-Means and EM, for time series. The algorithm works by leveraging off the multi-resolution property of wavelets. The dilemma of choosing the initial centers is mitigated by initializing the centers at each approximation level, using the final centers returned by the ...
Jessica Lin 0001 +3 more
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Fuzzy clustering of univariate and multivariate time series by genetic multiobjective optimization [PDF]
Given a set of time series, it is of interest to discover subsets that share similar properties. For instance, this may be useful for identifying and estimating a single model that may fit conveniently several time series, instead of performing the usual
S. Bandyopadhyay, R. Baragona, U. Maulik
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Advances in clustering and visualization of time series using GTM through time
, 2008 Most of the existing research on multivariate time series concerns supervised forecasting problems. In comparison, little research has been devoted to their exploration through unsupervised clustering and visualization. In this paper, the capabilities of
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Time series similarity measurement based on fractionaldifferential and its application
四川大学学报. 自然科学版, 2023 Similarity measures of time series are the basis for time series clustering, classification and other related time series analysis. The traditional distance-based similarity measure ignores the possible temporal connections of time series and treats time
YAN Wen-Peng, WANG Zhi-Tao, YUAN Xiao
doaj
A Novel Combination of Segmentation, Ensemble Clustering and Genetic Algorithm for Clustering Time Series [PDF]
Journal of Artificial Intelligence and Data MiningIncreasing the accuracy of time-series clustering while reducing execution time is a primary challenge in the field of time-series clustering. Researchers have recently applied approaches, such as the development of distance metrics and dimensionality ...
Zahra Ghorbani, Ali Ghorbanian
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Fuzzy cluster-aware contrastive clustering for time series
Pattern RecognitionThe rapid growth of unlabeled time series data, driven by the Internet of Things (IoT), poses significant challenges in uncovering underlying patterns. Traditional unsupervised clustering methods often fail to capture the complex nature of time series data.
Congyu Wang +3 more
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Efficient Time-Series Clustering through Sparse Gaussian Modeling
AlgorithmsIn this work, we consider the problem of shape-based time-series clustering with the widely used Dynamic Time Warping (DTW) distance. We present a novel two-stage framework based on Sparse Gaussian Modeling.
Dimitris Fotakis +3 more
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Clustering life trajectories: A new divisive hierarchical clustering algorithm for discrete-valued discrete time series [PDF]
A new algorithm for clustering life course trajectories is presented and tested with large register data. Life courses are represented as sequences on a monthly timescale for the working-life with an age span from 16-65.
Dlugosz, Stephan
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