Алгоритм и особенности построения кластеров в туризме
The article targets to consider the process, peculiarities and principles of cluster formation on the example of the tourism industry. The author proved the inter-sectoral relationship within the touristic cluster. Proposed the algorithm for the touristic cluster formation on the basis of tourist destinations. Distinctive features of the cluster in tourism are revealed and recommendations of clusters in domestic tourism are given.
The article describes clusters as a mechanism for economic growth and innovation in the region. The author considers the approach to the definition of a cluster as normative legal acts, and in the scientific literature, the advantages of cluster development are defined, and the cumulative effects of interaction between organizations within clusters are described. The information on the development institutions of clusters is presented on the example St. Petersburg.
One of the goals of the first edition of this book back in 2005 was to present a coherent theory for K-Means partitioning and Ward hierarchical clustering. This theory leads to effective data pre-processing options, clustering algorithms and interpretation aids, as well as to firm relations to other areas of data analysis. The goal of this second edition is to consolidate, strengthen and extend this island of understanding in the light of recent developments. Moreover, the material on validation and interpretation of clusters is updated with a system better reflecting the current state of the art and with our recent ``lifting in taxonomies'' approach. The structure of the book has been streamlined by adding two Chapters: ``Similarity Clustering'' and ``Validation and Interpretation'', while removing two chapters: ``Different Clustering Approaches'' and ``General Issues.'' The Chapter on Mathematics of the data recovery approach, in a much extended version, almost doubled in size, now concludes the book. Parts of the removed chapters are integrated within the new structure. The change has added a hundred pages and a couple of dozen examples to the text and, in fact, transformed it into a different species of a book. In the first edition, the book had a Russian doll structure, with a core and a couple of nested shells around. Now it is a linear structure presentation of the data recovery clustering.
Initially proposed by Charnes, Cooper and Rhodes as a method for comparative efficiency assessment, Data Envelopment Analysis (DEA) eventually got an alternative use. Researchers suggested ways to use it to group (cluster) objects not by the level of their efficiency, but by other parameters, which, from the computational point of view, were secondary results of applying DEA determining the mode used by the object to gain efficiency. The need for such an approach is dictated by two research objectives in strategic management, requiring clustering companies as objects of analysis. First, as companies follow different lines of behavior, finding stable patterns of their actions, and explaining and predicting their behavior is possible only when companies are broken into homogeneous groups. Second, comparative assessment of companies’ success is also possible only within homogeneous groups, because changes in such indicators as unit costs, market share, sales per employee and other similar measures may be assessed quite differently depending on whether the company in question is aspiring to gain the wide market through cost leadership or is following an alternative pathway. Authors undertake a comparative analysis of the two approaches to clustering production facilities based on DEA results. Po, Guh and Yang suggested combining in the same cluster objects with the same production function, when isoquants are determined by the production probability area. Alternative methods based on application of standard clustering procedures to DEA results have been proposed by Kao and Hung, and later by Volkova, Filinov, Titova, Kuskova, Gorny and Nikolaeva. Theoretical analysis and computational experiments show that both approaches (based on finding the edges of the production probability area and based on application of standard clustering procedures to DEA results) yield similar results under certain circumstances but differ in the opportunities offered to the researcher in substantive interpreting of the groups created and performing alternative calculations with the changing number of clusters (groups).
The central focus of this paper is a methodological one. Using the set of indicators of state capacity, we demonstrate a specific strategy for identifying sustainable structures in multidimensional data sets that reflect complex and ambiguous concepts of political science. A key feature of this strategy is the application of related, but significantly different technically, multidimensional methods – cluster and pattern analyses. We use hierarchical clustering with various combinations of metrics and amalgamation rules, as well as ordinal-invariant pattern-clustering. Properties of pattern analysis as a method for studying multidimensional data are shown for the first time (to the best of our knowledge) in the political science literature. Since clustering has been actively used in political science for a long time, pattern analysis is still practically not adopted in our science. This situation requires correction, since pattern-analysis has some important and in many ways unique capabilities. It was shown that the combination of pattern and cluster analyses makes it possible to identify consistent structures that have a clear interpretation in terms of political science. Thus, in the course of our study, several types of state capacity were identified (although this task was rather illustrative for us). We use a set of empirical indicators of state capacity: the share of military spending in GDP, the share of military personnel in the total population, the share of tax revenues in GDP, the total rate of homicides and victims of internal conflicts, and the quality of government institutions. Data for more than 150 countries are taken for 1996, 2005 and 2015. Stable combinations of the values of these indicators, identified simultaneously via pattern and cluster analyses, form the structures of state capacity. In conclusion, we show the most promising directions for the development of the methodology described in this paper. One of the most important is the analysis of the dynamics of countries within the pattern-cluster structures of state capacity.
This article examines the evolution of the significance of cluster territories in resource - driven economies. Authors provides an analysis of factors in turning a territory into a habitat for an industrial cluster. Authors proposes stages in transforming an industrial cluster into an innovation cluster based on saturating the base territory with spatially affined production and scientific units, strong direct and indirect relations, and intensive knowledge flows. The outcome of geographic concentration is expected to be the cluster synergy effects, which "turns into" the cumulative territory effect with reflection in positive social - economic processes. Authors have conducted the testing of particular cluster territories for the intensity of using a cluster territory.
Smoking is a problem, bringing signifi cant social and economic costs to Russiansociety. However, ratifi cation of the World health organization Framework conventionon tobacco control makes it possible to improve Russian legislation accordingto the international standards. So, I describe some measures that should be taken bythe Russian authorities in the nearest future, and I examine their effi ciency. By studyingthe international evidence I analyze the impact of the smoke-free areas, advertisementand sponsorship bans, tax increases, etc. on the prevalence of smoking, cigaretteconsumption and some other indicators. I also investigate the obstacles confrontingthe Russian authorities when they introduce new policy measures and the public attitudetowards these measures. I conclude that there is a number of easy-to-implementanti-smoking activities that need no fi nancial resources but only a political will.
One of the most important indicators of company's success is the increase of its value. The article investigates traditional methods of company's value assessment and the evidence that the application of these methods is incorrect in the new stage of economy. So it is necessary to create a new method of valuation based on the new main sources of company's success that is its intellectual capital.