A fast partitioning algorithm and its application to earthquake investigation

Scitovski, Rudolf; Scitovski, Sanja

doi:10.1016/j.cageo.2013.06.010

Cited by 46 publications

(22 citation statements)

References 29 publications

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“…It is shown that the Least Absolute Deviation approach is much less sensitive to the presence of outliers compared to the Least Squares approach. In further research, instead of multiple launching of the k-median algorithm, we will endeavor to construct an efficient method for finding a nearly global optimal partition based on the the method described in [21], and a method which automatically determines the appropriate number of clusters in a partition.…”

Section: Discussionmentioning

confidence: 99%

Color image segmentation based on intensity and hue clustering - a comparison of LS and LAD approaches

Taler¹,

Sabo²

2014

Croat. Oper. Res. Rev.

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Abstract. Motivated by the method for color image segmentation based on intensity and hue clustering proposed in [26] we give some theoretical explanations for this method that directly follows from the natural connection between the maximum likelihood approach and Least Squares or Least Absolute Deviations clustering optimality criteria. The method is tested and illustrated on a few typical situations, such as the presence of outliers among the data.

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Section: Discussionmentioning

confidence: 99%

Color image segmentation based on intensity and hue clustering - a comparison of LS and LAD approaches

Taler¹,

Sabo²

2014

Croat. Oper. Res. Rev.

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“…Even after a few iterations this method will give a good initial approximation, and after that Algorithm 1 can very quickly find the global minimum of the function Φ and the globally optimal partition. Useful numerical methods for searching for a good approximation of a globally optimal partition can be found in the works of Bagirov and Ugon (2005), Bagirov et al (2011) or Scitovski and Scitovski (2013).…”

Section: Numerical Examplesmentioning

confidence: 99%

Center-based l1–clustering method

Sabo

2014

International Journal of Applied Mathematics and Computer Science

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In this paper, we consider the l1-clustering problem for a finite data-point set which should be partitioned into k disjoint nonempty subsets. In that case, the objective function does not have to be either convex or differentiable, and generally it may have many local or global minima. Therefore, it becomes a complex global optimization problem. A method of searching for a locally optimal solution is proposed in the paper, the convergence of the corresponding iterative process is proved and the corresponding algorithm is given. The method is illustrated by and compared with some other clustering methods, especially with the l2-clustering method, which is also known in the literature as a smooth k-means method, on a few typical situations, such as the presence of outliers among the data and the clustering of incomplete data. Numerical experiments show in this case that the proposed l1-clustering algorithm is faster and gives significantly better results than the l2-clustering algorithm.

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“…According to Reyes and Cárdenas [46], Scitovski and Scitovski [47], who performed a Chilean and Croatian seismic zonification in 2010 and 2013, respectively, the features generated are:…”

Section: Data Transformationmentioning

confidence: 99%

A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula

Martínez‐Álvarez

Gutiérrez‐Avilés

Morales‐Esteban

et al. 2015

Entropy

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A previous definition of seismogenic zones is required to do a probabilistic seismic hazard analysis for areas of spread and low seismic activity. Traditional zoning methods are based on the available seismic catalog and the geological structures. It is admitted that thermal and resistant parameters of the crust provide better criteria for zoning. Nonetheless, the working out of the rheological profiles causes a great uncertainty. This has generated inconsistencies, as different zones have been proposed for the same area. A new method for seismogenic zoning by means of triclustering is proposed in this research. The main advantage is that it is solely based on seismic data. Almost no human decision is made, and therefore, the method is nearly non-biased. To assess its performance, the method has been applied to the Iberian Peninsula, which is characterized by the occurrence of small to moderate magnitude earthquakes. The catalog of the National Geographic Institute of Spain has been used. The output map is checked for validity with the geology. Moreover, a Entropy 2015, 17 5001 geographic information system has been used for two purposes. First, the obtained zones have been depicted within it. Second, the data have been used to calculate the seismic parameters (b-value, annual rate). Finally, the results have been compared to Kohonen's self-organizing maps.

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A fast partitioning algorithm and its application to earthquake investigation

Cited by 46 publications

References 29 publications

Color image segmentation based on intensity and hue clustering - a comparison of LS and LAD approaches

Color image segmentation based on intensity and hue clustering - a comparison of LS and LAD approaches

Center-based l1–clustering method

A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula

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