Дата майнинга

дата майнинга

Learn how data mining uses machine learning, statistics and artificial intelligence to look for same patterns across a large universe of data. Many translated example sentences containing "data mining" – Russian-English dictionary and search engine for Russian translations. Data Mining: применение, примеры задач и внедрение. Ниже приведены некоторые примеры применения STATISTICA Data Miner для решения практических задач Data.

Дата майнинга

Платформа Viettel Data Mining была сотворена Центром киберпространства Viettel с целью посодействовать властям и деловым кругам лучше владеть надлежащими данными. Нгуен Чонг Дыонг — замглавы Управления информационных технологий Министерства инфы и коммуникаций Вьетнама отметил: «Мы призываем большие компании представить фаворитные инструменты и продукты, которые сумеют принести наивысшую пользу деловым кругам и гражданам.

Компания Viettel является одним из локомотивов воплощения цифровой трансформации в стране. Её новенькая платформа существенно поможет компаниям и организациям убыстрить цифровую трансформацию». VOVWORLD - Эта платформа была сотворена в рамках реализации утверждённой премьер-министром Вьетнама государственной программы цифровой трансформации до года и на перспективу до года. Добавить комментарий.

Премьер-министр Фам Минь Тинь: Гибкое управление денежно-кредитной политикой для поддержания экономической стабильности Развитие сельского хозяйства для сокращения разрыва меж городками и деревнями Наиболее 1 млн человек выздоровели от COVID Вьетнам и Наша родина договорились усилить оборонное сотрудничество.

Притягивающий запах рисовой лапши с мидиями «Бунхэн». Data mining helps educators access student data, predict achievement levels and pinpoint students or groups of students in need of extra attention. Aligning supply plans with demand forecasts is essential, as is early detection of problems, quality assurance and investment in brand equity.

Manufacturers can predict wear of production assets and anticipate maintenance, which can maximize uptime and keep the production line on schedule. Automated algorithms help banks understand their customer base as well as the billions of transactions at the heart of the financial system. Data mining helps financial services companies get a better view of market risks, detect fraud faster , manage regulatory compliance obligations and get optimal returns on their marketing investments.

Large customer databases hold hidden customer insight that can help you improve relationships, optimize marketing campaigns and forecast sales. Through more accurate data models, retail companies can offer more targeted campaigns — and find the offer that makes the biggest impact on the customer. Descriptive Modeling : It uncovers shared similarities or groupings in historical data to determine reasons behind success or failure, such as categorizing customers by product preferences or sentiment.

Sample techniques include:. Predictive modeling also helps uncover insights for things like customer churn , campaign response or credit defaults. Prescriptive Modeling : With the growth in unstructured data from the web, comment fields, books, email, PDFs, audio and other text sources, the adoption of text mining as a related discipline to data mining has also grown significantly.

You need the ability to successfully parse, filter and transform unstructured data in order to include it in predictive models for improved prediction accuracy. In the end, you should not look at data mining as a separate, standalone entity because pre-processing data preparation, data exploration and post-processing model validation, scoring, model performance monitoring are equally essential.

Prescriptive modelling looks at internal and external variables and constraints to recommend one or more courses of action — for example, determining the best marketing offer to send to each customer. Инсайты Углубленная аналитика. Data Mining Solutions. Why is data mining important? Data mining allows you to: Sift through all the chaotic and repetitive noise in your data. Understand what is relevant and then make good use of that information to assess likely outcomes.

Accelerate the pace of making informed decisions. The intersection of big data and data mining Data mining expert Jared Dean wrote the book on data mining. Heavy Reading: Advanced Predictive Network Analytics Learn how you can optimize the network by using predictive analytics to evaluate network performance — as well as fine-tune capacity and provide more targeted marketing. Data mining is at the heart of analytics efforts across a variety of industries and disciplines. Insurance With analytic know-how, insurance companies can solve complex problems concerning fraud, compliance, risk management and customer attrition.

Education With unified, data-driven views of student progress, educators can predict student performance before they set foot in the classroom — and develop intervention strategies to keep them on course. Manufacturing Aligning supply plans with demand forecasts is essential, as is early detection of problems, quality assurance and investment in brand equity. Banking Automated algorithms help banks understand their customer base as well as the billions of transactions at the heart of the financial system.

Retail Large customer databases hold hidden customer insight that can help you improve relationships, optimize marketing campaigns and forecast sales. We do a better job of analyzing what we really need to analyze and predicting what we really want to predict. How It Works Data mining, as a composite discipline, represents a variety of methods or techniques used in different analytic capabilities that address a gamut of organizational needs, ask different types of questions and use varying levels of human input or rules to arrive at a decision.

Sample techniques include: Clustering Grouping similar records together. Anomaly detection Identifying multidimensional outliers. Association rule learning Detecting relationships between records. Principal component analysis Detecting relationships between variables. Affinity grouping Grouping people with common interests or similar goals e.

Sample techniques include: Regression A measure of the strength of the relationship between one dependent variable and a series of independent variables. Neural networks Computer programs that detect patterns, make predictions and learn.

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Data Mining Fundamentals

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