CC BY
7
№ 11 (116)
AunÎ
/Ш. TE)
UNIVERSUM:
ТЕХНИЧЕСКИЕ НАУКИ
ноябрь, 2023 г.
RELEVANCE OF DATA MINING TECHNOLOGIES AS A MEANS OF PROCESSING LARGE AMOUNTS OF INFORMATION
Otabek Ergashev
Assistant
of the Fergana branch of the Tashkent University of Information Technologies, Republic of Uzbekistan, Fergana E-mail: [email protected]
Muzaffarzhon Sobirov
Assistant
of the Fergana branch of the Tashkent University of Information Technologies, Republic of Uzbekistan, Fergana
АКТУАЛЬНОСТЬ ТЕХНОЛОГИЙ DATA MINING КАК СРЕДСТВА ОБРАБОТКИ БОЛЬШИХ ОБЪЕМОВ ИНФОРМАЦИИ
Эргашев Отабек Исмоилхон угли
ассистент
Ферганского филиала Ташкентского университета информационных технологий, Республика Узбекистан, г. Фергана
Собиров Музаффаржон Мирзаолимович
ассистент
Ферганского филиала Ташкентского университета информационных технологий, Республика Узбекистан, г. Фергана
ABSTRACT
In the contemporary era, the exponential growth of data poses a formidable challenge to organizations seeking to derive meaningful insights from vast information repositories. This article explores the relevance of data mining technologies as a pivotal means of processing large amounts of information. Data mining, with its advanced algorithms and analytical techniques, enables the extraction of patterns, trends, and valuable knowledge from massive datasets, thereby facilitating informed decision-making. This paper delves into the fundamental concepts of data mining, its applications, and its transformative impact on diverse sectors such as finance, healthcare, and e-commerce. Through a comprehensive review, the article elucidates the significance of harnessing data mining technologies to navigate the complexities of big data and extract actionable intelligence.
АННОТАЦИЯ
В современную эпоху экспоненциальный рост данных представляет собой серьезную проблему для организаций, стремящихся получить значимую информацию из огромных информационных хранилищ. В этой статье иссле -дуется актуальность технологий интеллектуального анализа данных как основного средства обработки больших объемов информации. Интеллектуальный анализ данных с его передовыми алгоритмами и аналитическими методами позволяет извлекать закономерности, тенденции и ценные знания из огромных наборов данных, тем самым способствуя принятию обоснованных решений. В этой статье рассматриваются фундаментальные концепции интеллектуального анализа данных, его применения и его преобразующее влияние на различные сектора, такие как финансы, здравоохранение и электронная коммерция. Посредством всестороннего обзора статья поясняет важность использования технологий интеллектуального анализа данных для преодоления сложностей больших данных и извлечения действенной информации.
Keywords: Data Mining; Big Data; Information Processing; Analytics; Decision-Making.
Ключевые слова: Data Mining; Big Data; Обработка информации; Аналитика; Принятие решений.
Библиографическое описание: Ergashev O., Sobirov M. RELEVANCE OF DATA MINING TECHNOLOGIES AS A MEANS OF PROCESSING LARGE AMOUNTS OF INFORMATION // Universum: технические науки : электрон. научн. журн. 2023. 11(116). URL: https://7universum.com/ru/tech/archive/item/16325
№ 11 (116)
A UNÍ
/Ш. ТЕ)
UNIVERSUM:
ТЕХНИЧЕСКИЕ НАУКИ
ноябрь, 2023 г.
In the digital age, the volume and velocity of data generated daily have reached unprecedented levels, necessitating innovative approaches to distill meaningful insights. Data mining, a subset of the broader field of data analytics, has emerged as a powerful tool for uncovering hidden patterns and knowledge within massive datasets. This article seeks to underscore the relevance of data mining technologies in efficiently processing large amounts of information. By employing sophisticated algorithms, data mining not only aids in unraveling intricate relationships within data but also empowers organizations to make strategic decisions based on evidence and trends. As industries grapple with the challenges posed by the information deluge, understanding and embracing the capabilities of data mining becomes imperative for staying competitive and unlocking the full potential of available data resources [1, p. 10].
In today's digital age, the amount of data being generated and collected is growing at an exponential rate. This has led to an increasing need for tools and technologies that can effectively process and analyze large amounts of information. Data mining is one such technology that has proven to be invaluable in this regard. Data mining is the process of extracting useful information from large datasets. It involves using various statistical and machine learning techniques to uncover patterns, trends, and insights that can be used to make informed business decisions. Data mining technologies are designed to handle the massive amounts of data being generated in various industries, including finance, healthcare, retail, and manufacturing [2, p. 1-9]. The relevance of data mining technologies as a means of processing large amounts of information cannot be overstated. In today's data-driven world, organizations rely on data mining tools to gain insights into customer behavior, market trends, and operational efficiencies. These insights can help businesses improve their decision-making processes, develop new products and services, and gain a competitive edge in the market.
One of the key advantages of data mining technologies is their ability to uncover hidden patterns and correlations within large datasets. This allows businesses to identify opportunities for growth and optimization that may not be immediately apparent through traditional means. For example, in the retail industry, data mining can be used to analyze customer purchasing patterns and preferences, which can then be used to personalize marketing campaigns and improve customer satisfaction. Another important relevance of data mining technologies is their ability to handle complex and unstructured data. With the rise of big data, organizations are dealing with diverse sources of information, including text, images, and videos. Data mining technologies are able to process and analyze this unstructured data, extracting valuable insights that can provide meaningful business value. Moreover, data mining technologies are also instrumental in fraud detection and security. By analyzing large volumes of data, businesses can identify anomalies and patterns that may indicate fraudulent activities. This is particularly relevant in the finance and healthcare industries, where the detection of fraudulent behavior can have significant financial and ethical implications.
At its core, data mining is a multidisciplinary field that draws upon techniques from statistics, machine learning, and database management to transform raw data into actionable knowledge. The process involves the identification of hidden patterns and relationships within vast datasets, enabling organizations to make informed decisions, predict future trends, and gain a competitive edge. As businesses increasingly recognize the value embedded in their data, the application of data mining technologies has become pivotal in unlocking the latent potential within these information reservoirs. The relevance of data mining technologies spans across diverse industries, influencing decision-making processes and strategy formulation. In finance, data mining is employed to detect fraudulent transactions, assess credit risk, and optimize investment portfolios. Healthcare leverages data mining to enhance patient care, predict disease outbreaks, and personalize treatment plans. E-commerce platforms utilize these technologies for customer segmentation, recommendation systems, and targeted marketing [3, p. 19]. The applications are manifold, emphasizing the universality of data mining's impact. One of the primary contributions of data mining technologies lies in their transformative impact on decision-making. By extracting valuable insights from large datasets, organizations can make data-driven decisions, mitigating risks and capitalizing on opportunities. For instance, retailers can optimize inventory management by analyzing purchasing patterns, and manufacturers can enhance production efficiency through predictive maintenance models derived from data mining techniques [4].
In addition, the relevance of data mining technologies extends to the field of predictive analytics. By analyzing historical data, these technologies can be used to forecast future trends and behaviors. This can help businesses make informed decisions that can minimize risks and maximize opportunities. Overall, the relevance of data mining technologies as a means of processing large amounts of information is clear. In a world where data is king, organizations across all industries are turning to data mining technologies to extract valuable insights that can drive business growth and success. As the volume and complexity of data continue to grow, the demand for robust data mining tools will only increase, making them indispensable in the modern business landscape.
In conclusion, the relevance of data mining technologies as a means of processing large amounts of information is undeniable. As organizations grapple with the complexities of big data, the ability to extract meaningful insights from this wealth of information becomes a competitive advantage. Data mining not only facilitates efficient information processing but also empowers decision-makers to navigate the intricate landscape of contemporary data challenges. The transformative impact across industries underscores the need for a comprehensive understanding of data mining technologies and their responsible application in our data-centric world. As we continue to delve deeper into the digital age, the role of data mining will undoubtedly evolve, shaping the future of information processing and decision-making.
№ 11 (116)
UNIVERSUM:
ТЕХНИЧЕСКИЕ НАУКИ
ноябрь, 2023 г.
Список литературы:
1. Mishra R.N. and Mishra A. (2013). Relevance of data mining in digital library. International Journal of Future Computer and Communication, 2(1), 10.
2. Manickam, R., Boominath, D., and Bhuvaneswari, V. (2012). An Analysis of Data Mining: Past, Present and Future. International Journal of Computer Engineering & Technology (IJCET), 3(1), 1-9.
3. Jackson, J. (2002). Data mining; a conceptual overview. Communications of the Association for Information Systems, 8(1), 19.
4. Wu, W., Lu, X., Cox, B., Li, G., Lin, L., and Yang, Q. (2014, December). Retrieving information and discovering knowledge from unstructured data using big data mining technique: Heavy oil fields example. In International Petroleum Technology Conference (pp. IPTC-17805). IPTC.
