Results 171 to 180 of about 303,598 (207)

Characterization of time series for analyzing of the evolution of time series clusters

Expert Systems with Applications, 2015
We propose a characterization of time series for multivariable temporal databases.For the characterization of time-series we used the level and trend components.The characterization the time-series is adequate for long and short periods of time.Our proposal allows analysis the evolution of the groups and objects.We developed an R-based script for ...
Ana P. Serra, Luis E. Zárate
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Incremental fuzzy clustering of time series

Fuzzy Sets and Systems, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ling Wang, Peipei Xu, Qian Ma
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Time-series clustering – A decade review

Information Systems, 2015
Clustering is a solution for classifying enormous data when there is not any early knowledge about classes. With emerging new concepts like cloud computing and big data and their vast applications in recent years, research works have been increased on unsupervised solutions like clustering algorithms to extract knowledge from this avalanche of data ...
Saeed Aghabozorgi, Ali Seyed Shirkhorshidi, Teh Ying Wah   +2 more
exaly   +2 more sources

Time series clustering with ARMA mixtures

Pattern Recognition, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xiong, YM, Yeung, Dit Yan
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Optimizations in time series clustering and prediction

Proceedings of the 11th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing on International Conference on Computer Systems and Technologies, 2010
In this paper a combination of time series clustering and prediction is considered. Both clustering and prediction are done by neural networks with supervised and unsupervised learning respectively. Some optimizations of the clustering procedure are proposed for software implementation.
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Clustering of large time series datasets

Intelligent Data Analysis, 2014
Time series clustering is a very effective approach in discovering valuable information in various systems such as finance, embedded bio-sensor and genome. However, focusing on the efficiency and scalability of these algorithms to deal with time series data has come at the expense of losing the usability and effectiveness of clustering. In this paper a
Saeed Reza Aghabozorgi, Ying Wah Teh
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Fast and Accurate Time-Series Clustering

ACM Transactions on Database Systems, 2017
The proliferation and ubiquity of temporal data across many disciplines has generated substantial interest in the analysis and mining of time series. Clustering is one of the most popular data-mining methods, not only due to its exploratory power but also because it is often a preprocessing step or subroutine for other techniques.
John Paparrizos
exaly   +2 more sources

Cluster analysis of respiratory time series

Biological Cybernetics, 1978
We have investigated the respiratory control system with the hypothesis that, although many variables such as minute ventilation (VI), tidal volume (VT), breathing period (TT), inspiratory duration (TI), and expiratory duration (TE) may be observed, the controller functions more simply by manipulating only 2 or 3 of these.
J M, Adams, E O, Attinger, F M, Attinger
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Tiered Clustering for Time Series Data

2021
Clustering is an essential unsupervised learning method. While the clustering of discrete data is a reasonably solved problem, sequential data clustering, namely time series data, is still an ongoing problem. Sequential data such as time series is widely used due to its abundance of detailed information.
Ruizhe Ma, Rafal A. Angryk
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Evolutionary Hierarchical Time Series Clustering

Sixth International Conference on Intelligent Systems Design and Applications, 2006
Time series clustering is an important topic, particularly for similarity search amongst long time series such as those arising in bioinformatics. In this paper a new evolutionary algorithm for detecting the hierarchical structure of an input time series data set is proposed.
Monica Chis, Crina Grosan
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