Development Of Cluster Approach Research Articles

Development of cluster approach in tourism (Russia case study)

The concept of tourism clustering is currently in the focus of tourism development on national and regional levels. The research is based on international academic literature review and the analysis of Russian experience. The paper analyses the dynamics of the tourism poten-tial of the partner re- gions joined into the cluster .Velikaya Volga. (The Big Volga), based on the expert opinion. There is a cost-benefit analysis of the tourism industry in the regions included into the tourism cluster using statistical data. The analysis-based diagrams show the differentiation of the Volga regions in terms of tourism potential use. Econometric approach allowed studying the dependence of tour-ism po- tential indicators on the tourism cluster .Velikaya Volga. revenue. The results of the research allow grouping the regions included in the cluster depending on the efficiency of their tourism potential implementation. The research also shows the significance of the com-parative evaluation of tourism cluster potential. The comparative method used for the cluster analysis allows enhancing the devel- opment of the regional tourism.

Detecting modules in biological networks by edge weight clustering and entropy significance.

Detection of the modular structure of biological networks is of interest to researchers adopting a systems perspective for the analysis of omics data. Computational systems biology has provided a rich array of methods for network clustering. To date, the majority of approaches address this task through a network node classification based on topological or external quantifiable properties of network nodes. Conversely, numerical properties of network edges are underused, even though the information content which can be associated with network edges has augmented due to steady advances in molecular biology technology over the last decade. Properly accounting for network edges in the development of clustering approaches can become crucial to improve quantitative interpretation of omics data, finally resulting in more biologically plausible models. In this study, we present a novel technique for network module detection, named WG-Cluster (Weighted Graph CLUSTERing). WG-Cluster's notable features, compared to current approaches, lie in: (1) the simultaneous exploitation of network node and edge weights to improve the biological interpretability of the connected components detected, (2) the assessment of their statistical significance, and (3) the identification of emerging topological properties in the detected connected components. WG-Cluster utilizes three major steps: (i) an unsupervised version of k-means edge-based algorithm detects sub-graphs with similar edge weights, (ii) a fast-greedy algorithm detects connected components which are then scored and selected according to the statistical significance of their scores, and (iii) an analysis of the convolution between sub-graph mean edge weight and connected component score provides a summarizing view of the connected components. WG-Cluster can be applied to directed and undirected networks of different types of interacting entities and scales up to large omics data sets. Here, we show that WG-Cluster can be successfully used in the differential analysis of physical protein–protein interaction (PPI) networks. Specifically, applying WG-Cluster to a PPI network weighted by measurements of differential gene expression permits to explore the changes in network topology under two distinct (normal vs. tumor) conditions. WG-Cluster code is available at https://sites.google.com/site/paolaleccapersonalpage/.