ПРИНЦИПИ ПОБУДОВИ ХМАР ТЕГІВ ДАНИХ. (Ukrainian)

Visualization mechanisms for constructing terminological clouds are considered. An example of JSON, HTML, CSV, XLSX, XML, TXT is a list of file types and resources. Possibilities of extraction and storage of input data are analyzed. Studies of similar systems were performed, on the basis of which two optimal file types were selected, namely CSV and TXT. The approach of forming a list of keywords for scholarly publications or distinguishing the leading topics of different texts was discovered. If the need is to handle large collaborative texts, such as literary works, scientific articles, judgments, etc., it will be sufficient to use small web applications to build tag clouds. K-mean tag clouds are able to effectively identify key concepts, most commonly used words, and leading concepts. When comparing CSV and TXT formats, it was confirmed that the processing speed depends more on the amount of input than on the file structure. Hence, it can be argued that the use of one or the other format is conditioned by the user's choice. An analysis has been conducted that noted that the CSV format needs an upper line that specifies attributes. For the sake of correctness of the further analysis, the attributes should be specified and formed each successive row of data in strict order. Such a slight feature of the structure helps the researcher to navigate among the set of textual information, and in further processing the first line can be ignored. Unlike the previous format, the TXT format does not require the formation of the first line of attributes. This complicates the visual perception of the information available. It is not recommended to enter the attributes yourself, in the future, when processing it will affect the correctness of the clustering results in the negative. [ABSTRACT FROM AUTHOR]

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