One of the most challenging aspects of clustering is validation, which is the objective and quantitative assessment of clustering results. A number of different relative validity criteria have been proposed for the validation ...

An integrated framework for density-based cluster analysis, outlier detection, and data visualization is introduced in this article. The main module consists of an algorithm to compute hierarchical estimates of the level ...

Spatial data mining techniques enable the knowledge extraction from spatial databases. However, the high computational cost and the complexity of algorithms are some of the main problems in this area. This work proposes a ...

The estimation of modal parameters is a critical requirement in structural health monitoring, damage detection, design validation, among other topics. The most prevalent methodology for manual identification is via an ...

Hoy en d´ıa existen varios dispositivos tecnologicos en lo cual permiten extraer datos de ´ trayectorias GPS como son los Smartphone, GPS y sensores etc. Gracias a esta herramienta se puede almacenar en las bases de datos ...

The semi-supervised, density-based clustering algorithm SSDBSCAN extracts clusters of a given dataset from different density levels by using a small set of labeled objects. A critical assumption of SSDBSCAN is, however, ...

This work considers probability models for partitions of a set of n elements using a predictive approach, i.e., models that are specified in terms of the conditional probability of either joining an already existing cluster ...

We present a new dissimilarity, which combines connectivity and density information. Usually, connectivity and density are conceived as mutually exclusive concepts; however, we discuss a novel procedure to merge both ...