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    • What is Data Mining? Data mining refers to extracting or mining knowledge from large amounts of data. In other words, Data mining is the science, art, and technology of discovering large and complex bodies of data in order to discover useful patterns.
    • What are the different tasks of Data Mining? The following activities are carried out during data mining: Classification. Clustering. Association Rule Discovery.
    • Discuss the Life cycle of Data Mining projects? The life cycle of Data mining projects: Business understanding: Understanding projects objectives from a business perspective, data mining problem definition.
    • Explain the process of KDD? Data mining treat as a synonym for another popularly used term, Knowledge Discovery from Data, or KDD. In others view data mining as simply an essential step in the process of knowledge discovery, in which intelligent methods are applied in order to extract data patterns.
  1. Feb 2, 2024 · What is data mining? Data mining is a computational process for discovering patterns, correlations, and anomalies within large datasets. It applies various statistical analysis and machine...

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  2. Dec 23, 2016 · CREATE YOUR ACCOUNT ASK A QUESTION ... My blackberry priv got a problem when I ... BlackBerry 10 OS; Blackberry Android or IOS - privacy/data mining survey? 01-27-17 ...

  3. Mar 10, 2024 · This article contains the most popular and common interview questions for data mining and their detailed answers. These will help you crack any interview for a data scientist job. Let’s get started. What are the Common Data Mining Interview Questions and Answers? 1. What is Data Mining?

  4. 60 Data Mining interview questions. Only coding challenges. Reset progress. Topic progress: 0% Data Mining Fundamentals. 1. Define data mining and explain its importance in the modern data-driven world. Question. Answer: 2. What is the difference between data mining and data analysis ? Question. Answer: 3.

  5. Mar 7, 2024 · Learn how to answer common data mining interview questions, such as data mining techniques, data quality, model evaluation, and result presentation.

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