Clustering of Oscillating Dynamical Systems from Time Series Data Bases
نویسندگان
چکیده
The clustering of time series from oscillating dynamical systems requires appropriately selected features. We employed features stemming from methods of linear and nonlinear analysis of time series, such as autocorrelation and Lyapunov exponents, as well as features estimating oscillation characteristics. Optimal feature forward selection and standardization method, under the standard k-means clustering algorithm, were assessed using Monte Carlo simulations on known oscillating deterministic and stochastic systems. The clustering efficiency was measured by the corrected Rand index and the results showed the prevalence of oscillating-related features. The same setting was applied to real-world oscillating time series, i.e. epileptic electroencephalograms and optokinetic signals, giving good discrimination of pathological states.
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