Medoid-based clustering using ant colony optimization

The application of ACO-based algorithms in data mining has been growing over the last few years, and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works about unsupervised learning have focused on clustering, showing the potential of ACO-based techniques. However, there are still clustering areas that are almost unexplored using these techniques, such as medoid-based clustering. Medoid-based clustering methods are helpful—compared to classical centroid-based techniques—when centroids cannot be easily defined. This paper proposes two medoid-based ACO clustering algorithms, where the only information needed is the distance between data: one algorithm that uses an ACO procedure to determine an optimal medoid set (METACOC algorithm) and another algorithm that uses an automatic selection of the number of clusters (METACOC-K algorithm). The proposed algorithms are compared against classical clustering approaches using synthetic and real-world datasets

Extending the SACOC algorithm through the Nystrom method for dense manifold data analysis

Data analysis has become an important field over the last decades. The growing amount of data demands new analytical methodologies in order to extract relevant knowledge. Clustering is one of the most competitive techniques in this context.Using a dataset as a starting point, these techniques aim to blindly group the data by similarity. Among the different areas, manifold identification is currently gaining importance. Spectral-based methods, which are the mostly used methodologies in this area, are however sensitive to metric parameters and noise. In order to solve these problems, new bio-inspired techniques have been combined with different heuristics to perform the clustering solutions and stability, specially for dense datasets. Ant Colony Optimization (ACO) is one of these new bio-inspired methodologies. This paper presents an extension of a previous algorithm named Spectral-based ACO Clustering (SACOC). SACOC is a spectral-based clustering methodology used for manifold identification. This work is focused on improving this algorithm through the Nystrom extension. The new algorithm, named SACON, is able to deal with Dense Data problems.We have evaluated the performance of this new approach comparing it with online clustering algorithms and the Nystrom extension of the Spectral Clustering algorithm using several datasets

A multi-objective genetic graph-based clustering algorithm with memory optimization

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. H. D. Menéndez, D. F. Barrero, and D. Camacho, "A multi-objective genetic graph-based clustering algorithm with memory optimization", in 2013 IEEE Congress on Evolutionary Computation (CEC), 2013, pp. 3174 - 3181Clustering is one of the most versatile tools for data analysis. Over the last few years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the Spectral Clustering algorithm, which is based on graph cut: it initially generates a Similarity Graph using a distance measure and then uses its Graph Spectrum to find the best cut. Memory consuption is a serious limitation in that algorithm: The Similarity Graph representation usually requires a very large matrix with a high memory cost. This work proposes a new algorithm, based on a previous implementation named Genetic Graph-based Clustering (GGC), that improves the memory usage while maintaining the quality of the solution. The new algorithm, called Multi-Objective Genetic Graph-based Clustering (MOGGC), uses an evolutionary approach introducing a Multi-Objective Genetic Algorithm to manage a reduced version of the Similarity Graph. The experimental validation shows that MOGGC increases the memory efficiency, maintaining and improving the GGC results in the synthetic and real datasets used in the experiments. An experimental comparison with several classical clustering methods (EM, SC and K-means) has been included to show the efficiency of the proposed algorithm.This work has been partly supported by: Spanish Ministry of Science and Education under project TIN2010-19872

Diversifying focused testing for unit testing

Software changes constantly because developers add new features or modifications. This directly affects the effectiveness of the testsuite associated with that software, especially when these new modifications are in a specific area that no test case covers. This paper tackles the problem of generating a high quality test suite to cover repeatedly a given point in a program, with the ultimate goal of exposing faults possibly affecting the given program point. Both search based software testing and constraint solving offer ready, but low quality, solutions to this: ideally a maximally diverse covering test set is required whereas search and constraint solving tend to generate test sets with biased distributions. Our approach, Diversified Focused Testing (DFT), uses a search strategy inspired by GödelTest. We artificially inject parameters into the code branching conditions and use a bi-objective search algorithm to find diverse inputs by perturbing the injected parameters, while keeping the path conditions still satisfiable. Our results demonstrate that our technique, DFT, is able to cover a desired point in the code at least 90% of the time. Moreover, adding diversity improves the bug detection and the mutation killing abilities of the test suites. We show that DFT achieves better results than focused testing, symbolic execution and random testing by achieving from 3% to 70% improvement in mutation score and up to 100% improvement in fault detection across 105 software subjects

Cirugía de la columna lumbar degenerativa

En una realidad la gran demanda actual de fusiones de la columna lumbar. Los resultados clínicos obtenidos con la fusión posterolateral se ven claramente superados con las fusiones anteroposteriores. Se realiza una revisión bibliográfica de las diferentes formas de fusión de la columna lumbar y la reaparición del concepto de soporte de columna anterior. Se establecen las indicaciones y las ventajas de la fusión anteroposterior lumbar, describiéndose las posibles vías de abordaje posterior y anteriores abiertas y mínimamente invasivas. Se clasifican los modernos implantes utilizados para soporte de la columna anterior. Se detallan ventajas e inconvenientes de las vías anterior y posterior.It is a reality the increasing number of the lumbar fusions. Clinical results show that the anteroposterior lumbar fusions achieve greater success rates than the posterolateral ones. A paper review of the different ways of lumbar fusion is carried out whit the update of the anterior column support concept. Indications and advantages: posterior, anterior and minimal invasive ones. The last spine cages used for anterior column support art classified. Advantages and drawbacks of the both approaches anterior and posterior are studied

Electroreflectance spectroscopy in self-assembled quantum dots: lens symmetry

Modulated electroreflectance spectroscopy $\Delta R/R$ of semiconductor self-assembled quantum dots is investigated. The structure is modeled as dots with lens shape geometry and circular cross section. A microscopic description of the electroreflectance spectrum and optical response in terms of an external electric field (${\bf F}$) and lens geometry have been considered. The field and lens symmetry dependence of all experimental parameters involved in the $\Delta R/R$ spectrum have been considered. Using the effective mass formalism the energies and the electronic states as a function of ${\bf F}$ and dot parameters are calculated. Also, in the framework of the strongly confined regime general expressions for the excitonic binding energies are reported. Optical selection rules are derived in the cases of the light wave vector perpendicular and parallel to $$. Detailed calculation of the Seraphin coefficients and electroreflectance spectrum are performed for the InAs and CdSe nanostructures. Calculations show good agreement with measurements recently performed on CdSe/ZnSe when statistical distribution on size is considered, explaining the main observed characteristic in the electroreflectance spectra

The internationalisation of the Spanish SME sector

As part of a wider research program, we analysed the theoretical framework and the recent developments of the process of internationalisation (transnationalisation) of the small- and medium-sized enterprises in Spain. The paper highlights the main trends and barriers of this internationalisation process. Methodology included document analyses, interviews, and the analyses of statistical databases

A CO emission line from the optical and near-IR undetected submillimeter galaxy GN10

We report the detection of a CO emission line from the submillimiter galaxy (SMG) GN10 in the GOODS-N field. GN10 lacks any counterpart in extremely deep optical and near-IR imaging obtained with the Hubble Space Telescope and ground-based facilities. This is a prototypical case of a source that is extremely obscured by dust, for which it is practically impossible to derive a spectroscopic redshift in the optical/near-IR. Under the hypothesis that GN10 is part of a proto-cluster structure previously identified at z~4.05 in the same field, we searched for CO[4-3] at 91.4 GHz with the IRAM Plateau de Bure Interferometer, and successfully detected a line. We find that the most likely redshift identification is z=4.0424+-0.0013, based on: 1) the very low chance that the CO line is actually serendipitous from a different redshift; 2) a radio-IR photometric redshift analysis; 3) the identical radio-IR SED, within a scaling factor, of two other SMGs at the same redshift. The faintness at optical/near-IR wavelengths requires an attenuation of A_V~5-7.5 mag. This result supports the case that a substantial population of very high-z SMGs exists that had been missed by previous spectroscopic surveys. This is the first time that a CO emission line has been detected for a galaxy that is invisible in the optical and near-IR. Our work demonstrates the power of existing and planned facilities for completing the census of star formation and stellar mass in the distant Universe by measuring redshifts of the most obscured galaxies through millimeter spectroscopy.Comment: 5 pages, 4 figures. ApJ Letters in pres

Electronic structure of crystalline binary and ternary Cd-Te-O compounds

The electronic structure of crystalline CdTe, CdO, $\alpha$-TeO$_2$, CdTeO$_3$ and Cd$_3$TeO$_6$ is studied by means of first principles calculations. The band structure, total and partial density of states, and charge densities are presented. For $\alpha$-TeO$_2$ and CdTeO$_3$, Density Functional Theory within the Local Density Approximation (LDA) correctly describes the insulating character of these compounds. In the first four compounds, LDA underestimates the optical bandgap by roughly 1 eV. Based on this trend, we predict an optical bandgap of 1.7 eV for Cd$_3$TeO$_6$. This material shows an isolated conduction band with a low effective mass, thus explaining its semiconducting character observed recently. In all these oxides, the top valence bands are formed mainly from the O 2p electrons. On the other hand, the binding energy of the Cd 4d band, relative to the valence band maximum, in the ternary compounds is smaller than in CdTe and CdO.Comment: 13 pages, 15 figures, 2 tables. Accepted in Phys Rev

Medoid-based clustering using ant colony optimization

The application of ACO-based algorithms in data mining has been growing over the last few years, and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works about unsupervised learning have focused on clustering, showing the potential of ACO-based techniques. However, there are still clustering areas that are almost unexplored using these techniques, such as medoid-based clustering. Medoid-based clustering methods are helpful—compared to classical centroid-based techniques—when centroids cannot be easily defined. This paper proposes two medoid-based ACO clustering algorithms, where the only information needed is the distance between data: one algorithm that uses an ACO procedure to determine an optimal medoid set (METACOC algorithm) and another algorithm that uses an automatic selection of the number of clusters (METACOC-K algorithm). The proposed algorithms are compared against classical clustering approaches using synthetic and real-world datasets