{"id":97890,"date":"2020-10-22T20:43:19","date_gmt":"2020-10-22T18:43:19","guid":{"rendered":"https:\/\/prohoster.info\/blog\/administrirovanie\/razbiraemsya-v-chem-raznicza-mezhdu-data-mining-i-data-extraction"},"modified":"2021-02-01T11:42:45","modified_gmt":"2021-02-01T09:42:45","slug":"razbiraemsya-v-chem-raznicza-mezhdu-data-mining-i-data-extraction","status":"publish","type":"post","link":"https:\/\/prohoster.info\/et\/blog\/administrirovanie\/razbiraemsya-v-chem-raznicza-mezhdu-data-mining-i-data-extraction","title":{"rendered":"Selgitame v\u00e4lja, milles seisneb erinevus Data Miningi ja Data Extractioni vahel.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

\"Selgitame<\/a><\/noindex>
\nNeed for Data Science-related terms often confuses many people. Data Mining is frequently misunderstood as data extraction, but the truth is more complex. In this post, let's clarify Mining and understand the difference between Data Mining and Data Extraction.
\n<\/a><\/noindex><\/p>\n

What is Data Mining?<\/h2>\n

Data mining, also known as Knowledge Discovery in Databases (KDD)<\/strong>, is a method commonly used to analyze large datasets using statistical and mathematical methods to identify hidden patterns or trends and extract value from them.<\/p>\n

What can be done with Data Mining?<\/h2>\n

By automating the process, data mining tools<\/a><\/noindex> can scan databases and efficiently detect hidden patterns. For businesses, data mining is often used to uncover correlations and relationships in data, helping to make optimal business decisions.<\/p>\n

Application examples<\/h2>\n

After data mining gained popularity in the 1990s, companies across a wide range of industries, including retail, finance, healthcare, transportation, telecommunications, e-commerce, etc., began utilizing data mining techniques to gain insights from data. Data mining can help segment customers, detect fraud, forecast sales, and much more.<\/p>\n