This article provides a cluster analysis of the socio-economic status and development of districts in Lviv region for a period of five years. The indicators that have the highest impact on cluster formation are detected. The author proposes an approach which can serve as a basis for developing effective territory management tools based on statistical analysis of socio-economic indicators.
The number of clustering methods and algorithms were analysed and the peculiarities of their application were singled out. The main advantages of density based clustering methods are the ability to detect free-form clusters of different sizes and resistance to noise and emissions, and the disadvantages include high sensitivity to input parameters, poor class description and unsuitability for large data. The analysis showed that the main problem of all clustering algorithms is their scalability with increasing amount of processed data.
The features of using the method of optimal scheme reduction for islanding of power
systems is reviewed. A modified algorithm for series-parallel folding with the formation of
hierarchically nested clusters is offered. The advantages of the algorithm is grounded and its
implementation in the application for islanding of power system is described.
Modern Word Wide Web contains a large number of Web sites and pages in each Web site. Web recommendation system (recommendation system for web pages) are typically implemented on web servers and use the data obtained from the collection viewed web templates (implicit data) or user registration data (explicit data). In article considering methods and algorithms of web recommendation system based on the technology of data mining (web mining).
In modern conditions, the role of industrial potential is increasing, which will ensure the modernization of the national economy. The emergence of crisis phenomena of clusterization in the industrial sector of the state is deepening due to the lack of directions in the management of its development.
In this article the strategic directions and program-targeted measures to provide the competitiveness of enterprises of dairy industry as well as the implementation mechanisms are analyzed. With this purpose, the clustering of the technological process of milk production, its processing and goods tracking in the supply chain of milk are suggested with using CPFR.
In the paper, combined self-learning and learning method of self-organizing map (SOM-LVQ) is proposed. Such method allows to increase quality of information processing under condition of overlapping classes due to rational choice of learning rate parameter and introducing special procedure of fuzzy reasoning in the clustering-classification process, which occurs both with external learning signal (“supervised”), and without one (“unsupervised”).