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DEVELOPMENT STAGES AND AREAS OF APPLICATION OF
DATA MINING
Akhmedov Abror Abdimajid ugli,
Karshi State Technical University,
Student of the Department of Telecommunication Technologies
Annotation. The article presents information about the methods and stages of
development of data intelligence analysis, which allows using modern technologies to
effectively analyze data and extract useful knowledge from it, and how the updating of
technologies and methods helps to make the analysis more accurate and effective. With
new technologies and innovations, the application will be further expanded, and it will
be possible to achieve effective results in various aspects of life around the world.
Key words: Intelligent data analysis, modern technologies, useful knowledge,
stages of development, technologies, methods, economics, healthcare, innovations.
Аннотация. В статье представлена информация о методах и этапах
развития анализа данных разведки, позволяющих с помощью современных
технологий эффективно анализировать данные и извлекать из них полезные
знания, а также о том, как обновление технологий и методов помогает сделать
анализ более точным и эффективным. С новыми технологиями и инновациями
применение будет еще больше расширяться, и можно будет добиться
эффективных результатов в различных аспектах жизни по всему миру.
Ключевые слова: Интеллектуальный анализ данных, современные
технологии, полезные знания, этапы развития, технологии, методы, экономика,
здравоохранение, инновации.
Data mining (DMM) is the process of extracting useful knowledge from large,
complex, and diverse data sets. DMM analyzes data, identifies patterns and
relationships, makes predictions, and extracts insights to support decision-making
processes. This process is mainly based on artificial intelligence (AI), machine learning
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(ML), natural language processing (NLP), and other advanced technologies. DMM is
widely used in all industries today. This article provides a detailed understanding of
the stages of development of DMM and its main areas of application.
Stages of Development of DMM
The development process of DMM consists of several stages, and new
technologies and methods are used at each stage. Each of them allows for better
analysis of data and extraction of useful knowledge from it.
Phase 1: Data Collection and Storage (1950s-1980s)
The initial phase involved data collection and storage. With the development
of computer technology in the 1950s, data began to be stored electronically. During
this period, data was digitized in many industries, and database management systems
(DBMS) were used to store and analyze it. However, at this stage, advanced methods
for data analysis were lacking, and in most cases, the data was only available for
statistical analysis.
Stage 2: Data Analysis and Analysis (1980-2000)
In the 1980s, with the development of artificial intelligence and machine
learning methods, a new result came in data analysis. This period saw the introduction
of methods such as data classification, regression analysis, and clustering. More
analytical productions began to be used in the analysis, output, and decision-making.
At this stage, the technologies of “Data Mining” (data mining) and “Business
Analytics” (business analytics) appeared.
Stage 3: The development of machine learning and artificial intelligence
technology (2000-2010)
By the 2000s, new methods of artificial intelligence and machine learning, deep
learning, and deep learning technologies appeared. During this period, data analysis
and forecasting systems were able to handle not only static data, but also dynamic and
complex data. Machine learning algorithms and neural networks have helped to make
analysis more accurate and efficient. New techniques, such as image recognition,
natural language processing (NLP), and voice are now possible in areas such as.
Stage 4: Big Data and IoT Integration (2010 to Present)
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In recent years, with the integration of "Big Data" technologies and the Internet
of Things (IoT), the volume and diversity of data have entered a new stage. During this
period, the data to be analyzed is no longer only structured data, but also data in various
forms such as images, videos, sensor data, etc. The large volume and speed of data
require new artificial intelligence technologies, so analysis systems have begun to use
parallel computing and cloud technologies to improve efficiency. During this period,
more complete and accurate knowledge is extracted from data using advanced
algorithms such as deep learning and reinforcement learning.
Data mining is used in a number of industries today, and each industry requires
its own unique methods and approaches.
Business and Marketing: Data mining is an important tool for optimizing
business processes and developing marketing strategies. Companies use MIT to
analyze customer behavior, forecast sales, and provide personalized product
recommendations. Machine learning can be used to create customer-targeted
advertising campaigns and improve marketing effectiveness.
Finance: MIT technologies play a major role in financial analysis and risk
management. Banks and financial institutions use MIT to assess credit, assess risk for
investors, and forecast financial markets. For example, by analyzing credit history, it
is possible to predict whether a loan application will be successful or unsuccessful.
Healthcare: In medicine, MIT helps predict patient health, optimize treatment
plans, and quickly diagnose diseases. New discoveries are being made in the analysis
of medical images, the development of personalized treatments based on genetic data,
and disease prevention using artificial intelligence.
Transportation and Logistics: MIT is also used to optimize transportation
systems. In the automotive industry, MIT technologies are used, especially in the
development of driverless vehicles, traffic flow analysis, and the optimization of
freight transportation processes.
Industry and Manufacturing: MIT is used to analyze and optimize industrial
production processes. Machine learning and artificial intelligence algorithms can
reduce uncertainties in production processes, optimize energy consumption, and
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increase safety. In addition, it can increase the efficiency of maintenance and predict
problems that can be solved in advance.
Data intelligence analysis creates the opportunity to effectively analyze data
and extract useful knowledge from it using modern technologies. As shown in the
stages of development of MIT, technologies and methods are updated every year,
helping to make this analysis more accurate and effective. At the same time, MIT is
widely used in various fields, making revolutionary changes in many areas, from
economics to healthcare. With new technologies and innovations, the application of
MIT is expanding further, allowing to achieve effective results in various aspects of
life around the world.
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