Научный журнал «Инновации. Наука. Образование» Индексация в ринц н Инновации. Наука. Образование
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Номер 51 февраль 2022 года
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- List of literature
| Инновации. Наука. Образование
− Split the data into two equal parts randomly. Perform cluster analysis separately for each half. Compare cluster centroids of two subsamples. – Randomly delete some variables. Perform cluster analysis on a reduced set of variables. Compare the results with those obtained based on the full set of variables. The need for automatic grouping may arise in many cases, for example, when structuring data, with natural systematization, as well as for data compression by replacing similar or very similar data objects with one object that contains their average representation. Data recording and storage technologies are constantly being improved, so there is a need to process a large amount of information. The increasing use of large-dimensional data arrays stimulates increased interest in the development and application of methods and tools for processing and analyzing these arrays. This kind of aspect guarantees a high potential for the development of data analysis, their structuring, grouping and search methods. Most of the data arrays are either already stored in digital form, or are being intensively digitized. At the same time, the volume and quality of modern tools and solutions, including data collection, storage and processing systems, is increasing, and, as a result, the need for their qualitative analysis and reliable conclusions for making effective management decisions. All this requires new advances in the ways of perception, automatic processing and generalization of information. Internet pages, company credentials, and electronic messages store, update, and process huge amounts of memory, for which a "clear structure" is an unfamiliar concept. Increasing the volume and diversity of data requires the creation and development of new methodologies for processing and summarizing data. List of literature: 1. Zhuravlev Yu. I., Ryazanov V. V., Senko O. V. "Recognition". Mathematical methods. Software system. Practical applications. - M.: Phasis, 2006. ISBN 5-7036-0108-8. 2. Ruban A.I. Methods of data analysis, Krasnoyarsk: CPI KSTU, 2004. 319p. 3. Sovigolovko E.V. Methods for assessing the quality of clear clustering // Computer tools in education, 2011 - from 14 - 31. 4. Cherezov D. S., Tyukachev N. A. Overview of the main methods of classification and clustering of data // Bulletin of Voronezh. state University. Ser. "System analysis and information technologies". 2009. Issue 2. жүктеу/скачать 22,11 Mb. Достарыңызбен бөлісу:
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