Revealing of the specificity of mortgage housing lending based on the results of clustering of regions

Subject. This article deals with the particularities of the development of housing mortgage lending in the regions of Russia. Objectives. The article aims to substantiate the need for clustering of territorial entities by level of development of mortgage housing lending in Russia and test the most effective algorithm for mortgage clustering of regions. Methods. For the study, we used a systems approach, including scientific abstraction, analysis and synthesis, and statistical methods of data analysis. The algorithm k-medoids – Partitioning Around Medoids (PAM) was also used. Results. Based on the results of the study of regional statistics of the Russian Federation, the article reveals a significant asymmetry in the values of key socioeconomic indices that determine the level and dynamics of housing mortgages in the regions. This necessitates the clustering of territorial entities according to the level of development of mortgage housing lending in the country. To take into account the impact of various local conditions in assessing the prospects for the development of regional housing mortgages, the article proposes an indicator, namely, the integral regional mortgage affordability index. On its basis, in accordance with the selected clustering procedure, the article identifies five mortgage clusters in Russia and identifies their representative regions. Conclusions. Based on the analysis of the specificity of the development of regional mortgages in the Tula Oblast, taking into account the implementation of the target State programme, the article concludes that it is necessary to improve the mechanisms for financing regional mortgage programmes and justifies the need to develop differentiated programmes for the development of housing mortgages in groups of Russian regions.

FSHB Genotype Identified as a Relevant Diagnostic Parameter Revealed by Cluster Analysis of Men With Idiopathic Infertility

Introduction and ObjectivesAbout 30-75% of infertile men are diagnosed with idiopathic infertility, thereby lacking major causative factors to explain their impaired fertility status. In this study, we used a large cohort of idiopathic infertile men to determine whether subgroups could be identified by an unbiased clustering approach and whether underlying etiologic factors could be delineated.Patients and MethodsFrom our in-house database Androbase®, we retrospectively selected patients (from 2008 to 2018) with idiopathic male infertility (azoo- to normozoospermia) who fit the following selection criteria: FSH ≥ 1 IU/l, testosterone ≥ 8 nmol/l, ejaculate volume ≥ 1.5 ml. Patients with genetic abnormalities or partners with female factors were excluded.For the identified study population (n=2742), we used common andrologic features (somatic, semen and hormonal parameters, including the FSHB c.-211G>T (rs10835638) single nucleotide polymorphism) for subsequent analyses. Cluster analyses were performed for the entire study population and for two sub-cohorts, which were separated by total sperm count (TSC) thresholds: Cohort A (TSC ≥ 1 mill/ejac; n=2422) and Cohort B (TSC < 1 mill/ejac; n=320). For clustering, the partitioning around medoids method was employed, and the quality was evaluated by average silhouette width.ResultsThe applied cluster approach for the whole study population yielded two separate clusters, which showed significantly different distributions in bi-testicular volume, FSH and FSHB genotype. Cluster 1 contained all men homozygous for G (wildtype) in FSHB c.-211G>T (100%), while Cluster 2 contained most patients carrying a T allele (>96.6%). In the analyses of sub-cohorts A/B, two clusters each were formed too. Again, the strongest segregation markers between the respective clusters were bi-testicular volume, FSH and FSHB c.-211G>T.ConclusionWith this first unbiased approach for revealing putative subgroups within a heterogenous group of idiopathic infertile men, we did indeed identify distinct patient clusters. Surprisingly, across all diverse phenotypes of infertility, the strongest segregation markers were FSHB c.-211G>T, FSH, and bi-testicular volume. Further, Cohorts A and B were significantly separated by FSHB genotype (wildtype vs. T-allele carriers), which supports the notion of a contributing genetic factor. Consequently, FSHB genotyping should be implemented as diagnostic routine in patients with idiopathic infertility.

Time Series Clustering for Robust Mean-Variance Portfolio Selection: Comparison of Several Dissimilarity Measures

This paper shows how to create a robust portfolio selection with time series clustering by using some dissimilarity measure. Based on such dissimilarity measures, stocks are initially sorted into multiple clusters using the Partitioning Around Medoids (PAM) time series clustering approach. Following clustering, a portfolio is constructed by selecting one stock from each cluster. Stocks having the greatest Sharpe ratio are selected from each cluster. The optimum portfolio is then constructed using the robust Fast Minimum Covariance Determinant (FMCD) and robust S MV portfolio model. When there are a big number of stocks accessible for the portfolio formation process, we can use this approach to quickly generate the optimum portfolio. This approach is also resistant to the presence of any outliers in the data. The Sharpe ratio was used to evaluate the performance of the portfolios that were created. The daily closing price of stocks listed on the Indonesia Stock Exchange, which are included in the LQ-45 indexed from August 2017 to July 2018, was utilized as a case study. Empirical study revealed that portfolios constructed using PAM time series clustering with autocorrelation dissimilarity and a robust FMCD MV portfolio model outperformed portfolios created using other approaches.

A Novel Overlapping Method to Alleviate the Cold-Start Problem in Recommendation Systems

Recommendation systems (RSs) are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The main objective of RSs is to tool up users with desired items that meet their preferences. A major problem in RSs is called: “cold-start”; it is a potential problem called so in computer-based information systems which comprises a degree of automated data modeling. Particularly, it concerns the issue in which the system cannot draw any inferences nor have it yet gathered sufficient information about users or items. Since RSs performance is substantially limited by cold-start users and cold-start items problems; this research study takes the route for a major aim to attenuate users’ cold-start problem. Still in the process of researching, sundry studies have been conducted to tackle this issue by using clustering techniques to group users according to their social relations, their ratings or both. However, a clustering technique disregards a variety of users’ tastes. In this case, the researcher has adopted the overlapping technique as a tool to deal with the clustering technique’s defects. The advantage of the overlapping technique excels over others by allowing users to belong to multi-clusters at the same time according to their behavior in the social network and ratings feedback. On that account, a novel overlapping method is presented and applied. This latter is executed by using the partitioning around medoids (PAM) algorithm to implement the clustering, which is achieved by means of exploiting social relations and confidence values. After acquiring users’ clusters, the average distances are computed in each cluster. Thereafter, a content comparison is made regarding the distances between every user and the computed distances of the clusters. If the comparison result is less than or equal to the average distance of a cluster, a new user is added to this cluster. The singular value decomposition plus (SVD[Formula: see text]) method is then applied to every cluster to compute predictions values. The outcome is calculated by computing the average of mean absolute error (MAE) and root mean square error (RMSE) for every cluster. The model is tested by two real world datasets: Ciao and FilmTrust. Ultimately, findings have exhibited a great deal of insights on how the proposed model outperformed a number of the state-of-the-art studies in terms of prediction accuracy.

Consumer Ratings of the Most Desirable Hearing Aid Attributes

Background Modern hearing aids have various features and functionalities, such as digital wireless streaming, bilateral connectivity, rechargeability, and specialized programs, which allow for a multitude of hearing aid attributes (e.g., comfort, reliability, and clarity). Consumers likely vary greatly in their preferences for these hearing aid attributes. Their preferences might be related to various demographic and hearing loss characteristics. Purpose The purposes of this study were to describe which hearing aid attributes consumers find desirable when choosing their hearing aids and to explore factors that might predict preferences. Research Design Cross-sectional. Study Sample 14,993. Intervention Not applicable. Data Collection and Analysis In this retrospective study, hearing aid attribute preferences were evaluated from consumers who answered questions in the Help Me Choose tool on the HearingTracker.com Web site. Chi-squared tests and correlation analyses were used to identify potential relationships between attribute preference and respondent characteristics. Cluster analysis with Partitioning Around Medoids (PAM) was used to identify patterns of attribute preferences. Results Of the 21 hearing aid attributes queried, the four most favorably rated were improved ability to hear friends and family in quiet and in noisy settings, physical comfort, and reliability, with 75 to 88% of respondents rating these attributes as very or extremely important. Type of hearing loss, technology level preference, and mobile phone brand were significantly associated with preferences for all 21 hearing aid attributes. PAM cluster analysis unveiled two unique user groups based on their preference to hearing aid attributes. One-third of the respondents preferred high-end technology and favored all types of advanced attributes. The other two-thirds of users predominantly preferred either advanced or best match and were more selective about which attributes were most important to them. Conclusion Patterns in preferences to hearing aid attributes help identify unique subgroups of consumers. Patient preferences for specific hearing aid attributes, in addition to audiologic characteristics, could help audiologists in recommending hearing aids for their patients.

Overlapping community detection in networks based on link partitioning and partitioning around medoids

In this paper, we present a new method for detecting overlapping communities in networks with a predefined number of clusters called LPAM (Link Partitioning Around Medoids). The overlapping communities in the graph are obtained by detecting the disjoint communities in the associated line graph employing link partitioning and partitioning around medoids which are done through the use of a distance function defined on the set of nodes. We consider both the commute distance and amplified commute distance as distance functions. The performance of the LPAM method is evaluated with computational experiments on real life instances, as well as synthetic network benchmarks. For small and medium-size networks, the exact solution was found, while for large networks we found solutions with a heuristic version of the LPAM method.

Redefining Homogeneous Climate Regions in Bangladesh Using Multivariate Clustering Approaches

The knowledge of the climate pattern for a particular region is important to alleviate the impact of climate change and protect the environment by taking appropriate actions based on geographical knowledge. It is also equally important for water resources planning and management. In this study, the regional disparities and similarities have been revealed among different climate stations or regions in Bangladesh based on different climatological factors such as rainfall, temperatures, relative humidity, sea level pressure, cloud cover, wind speed, the sunshine hour. We have selected one of the best-fitted algorithms for particular climate data from three multivariate clustering approaches named hierarchical clustering, partitioning around medoids (PAM), and K-means clustering by using different validation tests. Four homogeneity tests (Mann-Kendall Test, Pettitt's test, Buishand Range Test, Standard Normal Homogeneity Test) also have been performed for each of the clusters created based on several factors. The results suggest that the climate regions or meteorological stations of Bangladesh can be clustered into two groups based on a combination of climatological variables. According to the findings, there is a huge variation between the two groups in terms of climatological factors. The first group (cluster 1) is located in the northern part of the country that includes drought-prone and vulnerable regions, whereas, the second group (cluster 2) contains rain-prone and hilly regions, which are mostly situated in the southern part. All newly defined clusters show homogeneous behavior with few exceptions such as clusters based on sea level pressure are not homogeneous.