Classification of clusters in the system of information support of the clustering strategy

The aim of research is to develop recommendations to classify clusters within establishment and development of information system for clustering strategy implementation at the regional level, country level, etc.The main results of research. It is found that nowadays different semantic content and number of features to classify clusters are used in scientific sources and practice. Advantages and disadvantages of clusters depending on the purpose and basic conditions of their formation are identified. Differentscientific approaches to typing of competitive clusters and clusters classification by development stages are compared.In June 2015, we interviewed 15 experts (businessmen, government officials, academics) about the highest priority features of clusters classification in the data base required for the development and monitoring of the clustering strategy. It was determined that the highest priority features must be: sectoral affiliation; intensity of innovation policy within the cluster; divisions given its geographical component; export potential; number of employees and development stage (corresponding stages of the life cycle of cluster), which received respectively 1,5; 1,5; 3; 4,5; 4,5 and 6 standardized ranks among the twenty features of classification that are most often found in scientific publications and described in this study.Conclusions and directions of further researches. It is revealed that today the forming of clusters in Ukraine is still at an early stage, although the international practice of clustering has long confirmed of its positive impact on economic growth in the regions where it is implemented. One reason of this situation is inadequate information and advisory support of cluster policy in our country. It is recommended to establish national and regional information centers to provide information about: cluster initiative; active clusters and their members; international, national and regional programs to support cluster initiatives. The information of these centers should be placed on specially designed web-sites with free access.The results of this research in the future may be useful for study of different clustering strategies and conditions for their selection in various market situations.