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Chaotic map clustering algorithm for EEG analysis

Bellotti, R; De Carlo, F; Stramaglia, S - In: Physica A: Statistical Mechanics and its Applications 334 (2004) 1, pp. 222-232

performance of the method has been compared with those obtained through parametric algorithms, as K-means and deterministic …

Persistent link: https://www.econbiz.de/10010589019

Clustering stock market companies via chaotic map synchronization

Basalto, N.; Bellotti, R.; De Carlo, F.; Facchi, P.; … - In: Physica A: Statistical Mechanics and its Applications 345 (2005) 1, pp. 196-206

A pairwise clustering approach is applied to the analysis of the Dow Jones index companies, in order to identify similar temporal behavior of the traded stock prices. To this end, the chaotic map clustering algorithm is used, where a map is associated to each company and the correlation...

Persistent link: https://www.econbiz.de/10010589671

An introduction to Monte Carlo methods

Walter, J.-C.; Barkema, G.T. - In: Physica A: Statistical Mechanics and its Applications 418 (2015) C, pp. 78-87

algorithms are more efficient. We introduce the rejection free (or continuous time) algorithm and describe in details an …

Persistent link: https://www.econbiz.de/10011077842

Generating random graphs with tunable clustering coefficients

Heath, Lenwood S.; Parikh, Nidhi - In: Physica A: Statistical Mechanics and its Applications 390 (2011) 23, pp. 4577-4587

neighbors of each other. We propose two algorithms, Conf and Throw, that take triangle and single edge degree sequences as input …

Persistent link: https://www.econbiz.de/10011060396