An overview of interactive visual data mining techniques for knowledge discovery

n the past decade, the analysis of data has faced the challenge of dealing with very large and complex datasets and the real-time generation of data. Technologies to store and access these complex and large datasets are in place. However, robust and scalable analysis technologies are needed to extract meaningful information from these datasets. The research field of Information Visualization and Visual Data Analytics addresses this need. Information visualization and data mining are often used complementary to each other. Their common goal is the extraction of meaningful information from complex and possibly large data. However, though data mining focuses on the usage of silicon hardware, visualization techniques also aim to access the powerful image-processing capabilities of the human brain. This article highlights the research on data visualization and visual analytics techniques. Furthermore, we highlight existing visual analytics techniques, systems, and applications including a perspective on the field from the chemical process industry.

The lottery-panel task for bi-dimensional parameter-free elicitation of risk attitudes

We propose first, a simple task for the eliciting attitudes toward risky choice, the SGG lottery-panel task, which consists in a series of lotteries constructed to compensate riskier options with higher risk-return trade-offs. Using Principal Component Analysis technique, we show that the SGG lottery-panel task is capable of capturing two dimensions of individual risky decision making i.e. subjects’ average risk taking and their sensitivity towards variations in risk-return. From the results of a large experimental dataset, we confirm that the task systematically captures a number of regularities such as: A tendency to risk averse behavior (only around 10% of choices are compatible with risk neutrality); An attraction to certain payoffs compared to low risk lotteries, compatible with over-(under-) weighting of small (large) probabilities predicted in PT and; Gender differences, i.e. males being consistently less risk averse than females but both genders being similarly responsive to the increases in risk-premium. Another interesting result is that in hypothetical choices most individuals increase their risk taking responding to the increase in return to risk, as predicted by PT, while across panels with real rewards we see even more changes, but opposite to the expected pattern of riskier choices for higher risk-returns. Therefore, we conclude from our data that an “economic anomaly” emerges in the real reward choices opposite to the hypothetical choices. These findings are in line with Camerer's (1995) view that although in many domains, paid subjects probably do exert extra mental effort which improves their performance, choice over money gambles is not likely to be a domain in which effort will improve adherence to rational axioms (p. 635). Finally, we demonstrate that both dimensions of risk attitudes, average risk taking and sensitivity towards variations in the return to risk, are desirable not only to describe behavior under risk but also to explain behavior in other contexts, as illustrated by an example. In the second study, we propose three additional treatments intended to elicit risk attitudes under high stakes and mixed outcome (gains and losses) lotteries. Using a dataset obtained from a hypothetical implementation of the tasks we show that the new treatments are able to capture both dimensions of risk attitudes. This new dataset allows us to describe several regularities, both at the aggregate and within-subjects level. We find that in every treatment over 70% of choices show some degree of risk aversion and only between 0.6% and 15.3% of individuals are consistently risk neutral within the same treatment. We also confirm the existence of gender differences in the degree of risk taking, that is, in all treatments females prefer safer lotteries compared to males. Regarding our second dimension of risk attitudes we observe, in all treatments, an increase in risk taking in response to risk premium increases. Treatment comparisons reveal other regularities, such as a lower degree of risk taking in large stake treatments compared to low stake treatments and a lower degree of risk taking when losses are incorporated into the large stake lotteries. Results that are compatible with previous findings in the literature, for stake size effects (e.g., Binswanger, 1980; Antoni Bosch-Domènech & Silvestre, 1999; Hogarth & Einhorn, 1990; Holt & Laury, 2002; Kachelmeier & Shehata, 1992; Kühberger et al., 1999; B. J. Weber & Chapman, 2005; Wik et al., 2007) and domain effect (e.g., Brooks and Zank, 2005, Schoemaker, 1990, Wik et al., 2007). Whereas for small stake treatments, we find that the effect of incorporating losses into the outcomes is not so clear. At the aggregate level an increase in risk taking is observed, but also more dispersion in the choices, whilst at the within-subjects level the effect weakens. Finally, regarding responses to risk premium, we find that compared to only gains treatments sensitivity is lower in the mixed lotteries treatments (SL and LL). In general sensitivity to risk-return is more affected by the domain than the stake size. After having described the properties of risk attitudes as captured by the SGG risk elicitation task and its three new versions, it is important to recall that the danger of using unidimensional descriptions of risk attitudes goes beyond the incompatibility with modern economic theories like PT, CPT etc., all of which call for tests with multiple degrees of freedom. Being faithful to this recommendation, the contribution of this essay is an empirically and endogenously determined bi-dimensional specification of risk attitudes, useful to describe behavior under uncertainty and to explain behavior in other contexts. Hopefully, this will contribute to create large datasets containing a multidimensional description of individual risk attitudes, while at the same time allowing for a robust context, compatible with present and even future more complex descriptions of human attitudes towards risk.

Complex extreme learning machine applications in terahertz pulsed signals feature sets

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets.

Foodomics for personalized nutrition: how far are we?

To effectively prevent the onset and reduce mortality from noncommunicable diseases, we must consider every individual as metabolically unique to allow for a personalized management to take place. Diet and gut microbiota are major components of the exposome that interact together with a genetic make-up in a complex interplay to result in an individual’s metabolic phenotype. In this context, foodomics approaches (such as nutrigenetics, nutrimetabolomics, nutritranscriptomics, nutriproteomics and metagenomics) are essential tools to assess an individual’s optimal metabolic space. These have recently been applied to large human cohorts to identify specific gene-metabolite, diet-metabolite and gene–diet interactions. As the gut microbiota is a key player in metabolic homeostasis, we suggest following a holistic investigation of metagenome–hyperbolome–diet interactions, the findings of which will provide the basis for developing personalized nutrition and personalized functional foods. However, examining these three-way interactions will only be possible when the challenge of large datasets integration will be overcome.

qtl.outbred: Interfacing outbred line cross data with the R/qtl mapping software

Background qtl.outbred is an extendible interface in the statistical environment, R, for combining quantitative trait loci (QTL) mapping tools. It is built as an umbrella package that enables outbred genotype probabilities to be calculated and/or imported into the software package R/qtl. Findings Using qtl.outbred, the genotype probabilities from outbred line cross data can be calculated by interfacing with a new and efficient algorithm developed for analyzing arbitrarily large datasets (included in the package) or imported from other sources such as the web-based tool, GridQTL. Conclusion qtl.outbred will improve the speed for calculating probabilities and the ability to analyse large future datasets. This package enables the user to analyse outbred line cross data accurately, but with similar effort than inbred line cross data.

MAPfastR : quantitative trait loci mapping in outbred line crosses

MAPfastR is a software package developed to analyze QTL data from inbred and outbred line-crosses. The package includes a number of modules for fast and accurate QTL analyses. It has been developed in the R language for fast and comprehensive analyses of large datasets. MAPfastR is freely available at: http://www.computationalgenetics.se/?page_id=7.

Recorte volumétrico usando técnicas de interação 2D e 3D

A visualização de conjuntos de dados volumétricos é comum em diversas áreas de aplicação e há já alguns anos os diversos aspectos envolvidos nessas técnicas vêm sendo pesquisados. No entanto, apesar dos avanços das técnicas de visualização de volumes, a interação com grandes volumes de dados ainda apresenta desafios devido a questões de percepção (ou isolamento) de estruturas internas e desempenho computacional. O suporte do hardware gráfico para visualização baseada em texturas permite o desenvolvimento de técnicas eficientes de rendering que podem ser combinadas com ferramentas de recorte interativas para possibilitar a inspeção de conjuntos de dados tridimensionais. Muitos estudos abordam a otimização do desempenho de ferramentas de recorte, mas muito poucos tratam das metáforas de interação utilizadas por essas ferramentas. O objetivo deste trabalho é desenvolver ferramentas interativas, intuitivas e fáceis de usar para o recorte de imagens volumétricas. Inicialmente, é apresentado um estudo sobre as principais técnicas de visualização direta de volumes e como é feita a exploração desses volumes utilizando-se recorte volumétrico. Nesse estudo é identificada a solução que melhor se enquadra no presente trabalho para garantir a interatividade necessária. Após, são apresentadas diversas técnicas de interação existentes, suas metáforas e taxonomias, para determinar as possíveis técnicas de interação mais fáceis de serem utilizadas por ferramentas de recorte. A partir desse embasamento, este trabalho apresenta o desenvolvimento de três ferramentas de recorte genéricas implementadas usando-se duas metáforas de interação distintas que são freqüentemente utilizadas por usuários de aplicativos 3D: apontador virtual e mão virtual. A taxa de interação dessas ferramentas é obtida através de programas de fragmentos especiais executados diretamente no hardware gráfico. Estes programas especificam regiões dentro do volume a serem descartadas durante o rendering, com base em predicados geométricos. Primeiramente, o desempenho, precisão e preferência (por parte dos usuários) das ferramentas de recorte volumétrico são avaliados para comparar as metáforas de interação empregadas. Após, é avaliada a interação utilizando-se diferentes dispositivos de entrada para a manipulação do volume e ferramentas. A utilização das duas mãos ao mesmo tempo para essa manipulação também é testada. Os resultados destes experimentos de avaliação são apresentados e discutidos.

Estudo Comparativo de Métricas de Pontuação para Aprendizagem Estrutural de Redes Bayesianas

Bayesian networks are powerful tools as they represent probability distributions as graphs. They work with uncertainties of real systems. Since last decade there is a special interest in learning network structures from data. However learning the best network structure is a NP-Hard problem, so many heuristics algorithms to generate network structures from data were created. Many of these algorithms use score metrics to generate the network model. This thesis compare three of most used score metrics. The K-2 algorithm and two pattern benchmarks, ASIA and ALARM, were used to carry out the comparison. Results show that score metrics with hyperparameters that strength the tendency to select simpler network structures are better than score metrics with weaker tendency to select simpler network structures for both metrics (Heckerman-Geiger and modified MDL). Heckerman-Geiger Bayesian score metric works better than MDL with large datasets and MDL works better than Heckerman-Geiger with small datasets. The modified MDL gives similar results to Heckerman-Geiger for large datasets and close results to MDL for small datasets with stronger tendency to select simpler network structures

Is it possible to make pixel-based radar image classification user-friendly?

In this paper we would like to shed light the problem of efficiency and effectiveness of image classification in large datasets. As the amount of data to be processed and further classified has increased in the last years, there is a need for faster and more precise pattern recognition algorithms in order to perform online and offline training and classification procedures. We deal here with the problem of moist area classification in radar image in a fast manner. Experimental results using Optimum-Path Forest and its training set pruning algorithm also provided and discussed. © 2011 IEEE.

Avaliação de modelos estatísticos considerando a incerteza da paternidade

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Visualização da informação colaborativa por meio de um ambiente multiprojetado e dispositivos móveis

Pós-graduação em Ciência da Computação - IBILCE

Improving gender classification accuracy in the wild

[EN]In this paper, we focus on gender recognition in challenging large scale scenarios. Firstly, we review the literature results achieved for the problem in large datasets, and select the currently hardest dataset: The Images of Groups. Secondly, we study the extraction of features from the face and its local context to improve the recognition accuracy. Diff erent descriptors, resolutions and classfii ers are studied, overcoming previous literature results, reaching an accuracy of 89.8%.

Machine-learning methods for structure prediction of β-barrel membrane proteins

Different types of proteins exist with diverse functions that are essential for living organisms. An important class of proteins is represented by transmembrane proteins which are specifically designed to be inserted into biological membranes and devised to perform very important functions in the cell such as cell communication and active transport across the membrane. Transmembrane β-barrels (TMBBs) are a sub-class of membrane proteins largely under-represented in structure databases because of the extreme difficulty in experimental structure determination. For this reason, computational tools that are able to predict the structure of TMBBs are needed. In this thesis, two computational problems related to TMBBs were addressed: the detection of TMBBs in large datasets of proteins and the prediction of the topology of TMBB proteins. Firstly, a method for TMBB detection was presented based on a novel neural network framework for variable-length sequence classification. The proposed approach was validated on a non-redundant dataset of proteins. Furthermore, we carried-out genome-wide detection using the entire Escherichia coli proteome. In both experiments, the method significantly outperformed other existing state-of-the-art approaches, reaching very high PPV (92%) and MCC (0.82). Secondly, a method was also introduced for TMBB topology prediction. The proposed approach is based on grammatical modelling and probabilistic discriminative models for sequence data labeling. The method was evaluated using a newly generated dataset of 38 TMBB proteins obtained from high-resolution data in the PDB. Results have shown that the model is able to correctly predict topologies of 25 out of 38 protein chains in the dataset. When tested on previously released datasets, the performances of the proposed approach were measured as comparable or superior to the current state-of-the-art of TMBB topology prediction.

Black Hole and Galaxy Growth over Cosmic Time: the Chandra COSMOS Legacy Survey

The study of supermassive black hole (SMBH) accretion during their phase of activity (hence becoming active galactic nuclei, AGN), and its relation to the host-galaxy growth, requires large datasets of AGN, including a significant fraction of obscured sources. X-ray data are strategic in AGN selection, because at X-ray energies the contamination from non-active galaxies is far less significant than in optical/infrared surveys, and the selection of obscured AGN, including also a fraction of heavily obscured AGN, is much more effective. In this thesis, I present the results of the Chandra COSMOS Legacy survey, a 4.6 Ms X-ray survey covering the equatorial COSMOS area. The COSMOS Legacy depth (flux limit f=2x10^(-16) erg/s/cm^(-2) in the 0.5-2 keV band) is significantly better than that of other X-ray surveys on similar area, and represents the path for surveys with future facilities, like Athena and X-ray Surveyor. The final Chandra COSMOS Legacy catalog contains 4016 point-like sources, 97% of which with redshift. 65% of the sources are optically obscured and potentially caught in the phase of main BH growth. We used the sample of 174 Chandra COSMOS Legacy at z>3 to place constraints on the BH formation scenario. We found a significant disagreement between our space density and the predictions of a physical model of AGN activation through major-merger. This suggests that in our luminosity range the BH triggering through secular accretion is likely preferred to a major-merger triggering scenario. Thanks to its large statistics, the Chandra COSMOS Legacy dataset, combined with the other multiwavelength COSMOS catalogs, will be used to answer questions related to a large number of astrophysical topics, with particular focus on the SMBH accretion in different luminosity and redshift regimes.

Phylogenetic approach reveals that virus genotype largely determines HIV set-point viral load

HIV virulence, i.e. the time of progression to AIDS, varies greatly among patients. As for other rapidly evolving pathogens of humans, it is difficult to know if this variance is controlled by the genotype of the host or that of the virus because the transmission chain is usually unknown. We apply the phylogenetic comparative approach (PCA) to estimate the heritability of a trait from one infection to the next, which indicates the control of the virus genotype over this trait. The idea is to use viral RNA sequences obtained from patients infected by HIV-1 subtype B to build a phylogeny, which approximately reflects the transmission chain. Heritability is measured statistically as the propensity for patients close in the phylogeny to exhibit similar infection trait values. The approach reveals that up to half of the variance in set-point viral load, a trait associated with virulence, can be heritable. Our estimate is significant and robust to noise in the phylogeny. We also check for the consistency of our approach by showing that a trait related to drug resistance is almost entirely heritable. Finally, we show the importance of taking into account the transmission chain when estimating correlations between infection traits. The fact that HIV virulence is, at least partially, heritable from one infection to the next has clinical and epidemiological implications. The difference between earlier studies and ours comes from the quality of our dataset and from the power of the PCA, which can be applied to large datasets and accounts for within-host evolution. The PCA opens new perspectives for approaches linking clinical data and evolutionary biology because it can be extended to study other traits or other infectious diseases.