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We introduce a novel adaptive version of the Neighborhood Retrieval Visualizer (NeRV). We maintain the advantages of the conventional NeRV method, while proposing an improvement of the data samples' neighborhood width calculation, in the input and output data space. In the standard NeRV, the data samples' neighborhood widths are determined in an arbitrary manner, in this way, inhibiting the possible quality of the resulting data visualization. We propose to compute the widths adaptively, on the basis of the input data scattering. Therefore, we first perform the preliminary input data clustering, next, we calculate the values of the inner-cluster variances, which convey the information on the input data scattering, then, we assign them to each data sample, and finally, we use them as the basis for the data samples' neighborhood widths determination. The results of the experiments conducted on the three different real datasets confirm the effectiveness and usefulness of the proposed approach. • An improved adaptive version of the Neighborhood Retrieval Visualizer is proposed. • The introduced visualization takes into account the input data scattering and adjusts to it correctly. • Data samples' neighborhood widths are determined on the basis of the inner-cluster variance of the data in the input space. • The inner-cluster variance is calculated after the preliminary data clustering carried out in the input data space. • The experimental research conducted on three real datasets confirmed the usefulness and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

We propose an asymmetric topology-preserving version of the Neighborhood Retrieval Visualizer (NeRV) dimensionality reduction and data visualization method. We introduce an asymmetric dissimilarity using the asymmetric coefficients. This dissimilarity is employed in the traditional NeRV, and in this way, a novel asymmetric form of this approach is formulated. The asymmetric coefficients convey the information on asymmetry in the input data. They are determined on the basis of the graph representing the input data. This allows for discovering of the geometry and topology of the input data. The experiments conducted on three real datasets confirm the effectiveness of the proposed approach on the basis of the comparative analysis of our solution against the six reference dimensionality reduction and data visualization algorithms. • We propose an asymmetric Neighborhood Retrieval Visualizer (NeRV). • Asymmetric NeRV is based on asymmetric dissimilarity using asymmetric coefficients. • Asymmetric coefficients are based on the graph of the input data. • Asymmetric coefficients extract the asymmetry, geometry, and topology of input data. • Experiments on three real datasets confirm the effectiveness of our method. [ABSTRACT FROM AUTHOR]

We propose a fraud detection method based on the user accounts visualization and threshold-type detection. The visualization technique employed in our approach is the Self-Organizing Map (SOM). Since the SOM technique in its original form visualizes only the vectors, and the user accounts are represented in our work as the matrices storing a collection of records reflecting the user sequential activities, we propose a method of the matrices visualization on the SOM grid, which constitutes the main contribution of this paper. Furthermore, we propose a method of the detection threshold setting on the basis of the SOM U-matrix. The results of the conducted experimental study on real data in three different research fields confirm the advantages and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract: We propose the use of an asymmetric dissimilarity measure in centroid-based clustering. The dissimilarity employed is the Alpha–Beta divergence (AB-divergence), which can be asymmetrized using its parameters. We compute the degree of asymmetry of the AB-divergence on the basis of the within-cluster variances. In this way, the proposed approach is able to flexibly model even clusters with significantly different variances. Consequently, this method overcomes one of the major drawbacks of the standard symmetric centroid-based clustering. [Copyright &y& Elsevier]

In this paper, a method for telecommunications fraud detection is proposed. The method is based on the user profiling using the Latent Dirichlet Allocation (LDA). Fraudulent behavior is detected with use of a threshold-type classification algorithm, allocating the telecommunication accounts into one of two classes: fraudulent account and non-fraudulent account. The paper provides also a method for automatic threshold computation. The accounts are classified with use of the Kullback-Leibler divergence (KL-divergence). Therefore, we also introduce three methods for approximating the KL-divergence between two LDAs. Finally, the results of experimental study on KL-divergence approximation and fraud detection in telecommunications are reported. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a method for telecommunications fraud detection is proposed. The method is based on the user profiling utilizing the Latent Dirichlet Allocation (LDA). Fraudulent behavior is detected with use of a threshold-type classification algorithm, allocating the telecommunication accounts into one of two classes: fraudulent account and non-fraudulent account. The paper provides also a method for automatic threshold computation. The accounts are classified with use of the Kullback–Leibler divergence (KL-divergence). Therefore, we also introduce three methods for approximating the KL-divergence between two LDAs. Finally, the results of experimental study on KL-divergence approximation and fraud detection in telecommunications are reported. [Copyright &y& Elsevier]

We propose a novel improved adaptive version of the Self-Organizing Map (SOM). Our enhancement of SOM adapts to the input data scattering, in this way, preserving the principal input data structure. In order to capture and extract the data scattering from the input dataset, we propose carrying out a preliminary input data clustering. As a result of forming the clusters in the input data space, the inner-cluster variances are calculated. The inner-cluster variances are subsequently employed as the basis for the determination of the neighborhood widths of the SOM's Best Matching Units (BMUs). The method, introduced in this paper, has been a subject to the empirical evaluation carried out using the three real-world datasets of different size, dimensionality, and data type, representing three various experimental fields. Our proposal has been assessed on the basis of a comparison with seven reference data visualization approaches. The introduced method appeared to be superior over the remaining investigated data visualization techniques, and consequently, its effectiveness, usefulness, and accuracy have been verified and confirmed. • A Self-Organizing Map (SOM) adapting to the input data scattering is proposed. • The input data scattering is expressed numerically by the inner-cluster variances. • The inner-cluster variances are obtained after the initial input data clustering. • The inner-cluster variances controle the neighborhood widths of the SOM's neurons. • The experiments on real-world data confirmed the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Background: Human papillomavirus (HPV) is the main etiological agent of cervical cancer, the third most common cancer among women globally and the second most frequent in Mexico. Persistent infection with high-risk HPV genotypes is associated with premalignant lesions and cervical cancer development. HPVs considered as low risk or not yet classified, are often found in coinfection with different HPV genotypes. Indeed, HPV62 is one of the most prevalent HPV detected in some countries, but there is limited information about its prevalence in other regions and there are no HPV62 variants currently described. The aim of this study was to determine the prevalence of HPV62 in cervical samples from Mexican women and to identify mutations in the L1, E6 and E7 genes, which have never been reported in our population. Methods: HPV screening was performed by Cobas HPV Test in women who attended prevention health programs and dysplasia clinics. All HPV positive samples ( = 491) and 87 additional cervical cancer samples were then n genotyped with Linear Array HPV Genotyping test. Some samples were selected to corroborate genotyping by Next-Generation sequencing. On the other hand, nucleotide changes in L1, E6 and E7 genes were determined using PCR, Sanger sequencing and analysis with the CLC-MainWorkbench 7.6.1 software. L1 protein structure was predicted with the I-TASSER server. Results: Using Linear Array, HPV62 prevalence was 7.6% in general population, 8% in Cervical Intraepithelial Neoplasia grade 1 (CIN1) samples and 4.6% in cervical samples. The presence of HPV62 was confirmed with Next-Generation sequencing. Regarding L1 gene, novel sequence variations were detected, but they did not alter the tertiary structure of the protein. Moreover, several nucleotide substitutions were found in E6 and E7 genes compared to reference HPV62 genomic sequence. Specifically, three non-synonymous sequence variations were detected, two in E6 and one in E7. Conclusions: HPV62 is a frequent HPV genotype found mainly in general population and in women with CIN1, and in 90.5% of the cases it was found in coinfection with other HPVs. Novel nucleotide changes in its and genes L1, E6 E7 were detected, some of them lead to changes in the protein sequence. [ABSTRACT FROM AUTHOR]

Background: Linear Array Genotyping Test (LA) is one of the gold standards used for Human Papillomavirus (HPV) genotyping, however, since its launching in 2006, new HPV genotypes are still being characterized with the use of high specificity techniques such as Next-Generation Sequencing (NGS). Derived from a previous study of the IMSS Research Network on HPV, which suggested that there might be cross-reaction of some HPV genotypes in the LA test, the aim of this study was to elucidate this point. Methods: Double stranded L1 fragments (gBlocks) from different HPVs were used to perform LA test, additionally, 14 HPV83+ and 26 HPV84+ cervical samples determined with LA, were individually genotyped by NGS. Results: From the LA HPV83+ samples, 64.3% were truly HPV83+, while 42.9% were found to be HPV102+. On the other hand, 69.2% of the LA HPV84+ samples were HPV84+, while 3.8, 11.5 and 30.8% of the samples were indeed HPV 86, 87 and 114 positive, respectively. Additionally, novel nucleotide changes in L1 gene from HPV genotypes 83, 84, 87, 102 and 114 were determined in Mexican cervical samples, some of them lead to changes in the protein sequence. Conclusions: We demonstrated that there is cross-hybridization between alpha3-HPV genotypes 86, 87 and 114 with HPV84 probe in LA strips and between HPV102 with HPV83 probe; this may be causing over or under estimation in the prevalence of these genotypes. In the upcoming years, a switch to more specific and sensitive genotyping methods that detect a broader spectrum of HPV genotypes needs to be implemented. [ABSTRACT FROM AUTHOR]