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Data Mining MCQ

  1. Data mining is a tool for allowing users to ...........
  2. The three Data Mining tasks are ............
  3. Data mining is a powerful new technology to ................
  4. Which of the following is not a data mining metric?
  5. Data mining helps in .............
  6. Capability of data mining is to build ___________ models.
  7. Which of the following is the other name of Data mining?
  8. Data can be updated in _____environment.
  9. .................. is the goal of data mining.
  10. Strategic value of data mining is ...................
  11. Removing duplicate records is a process called ................
  12. Data marts that incorporate data mining tools to extract sets of data are called ................
  13. Which of the following is a predictive model?
  14. Which of the following is a descriptive model?
Last Updated: Feb 01, 2023,
Posted in Interview Questions,
21 Questions
Data Mining MCQ

Practice Best Data Mining MCQ Questions

Practice Best Data Mining MCQ Questions, that checks your basic knowledge of Data Mining. This Data Mining MCQ Test contains 20+ Data Mining Multiple Choice Questions. So, practice these questions to check final preparation for your exams, or interviews. apart from this, you can also download below the Data Mining MCQ PDF, completely free.

  • find the hidden relationships in data
  • find the relationships in data
  • find the visible relationships in data
  • None of the above
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  • Classification
  • Clustering
  • Association Rules
  • All of the above
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  • Show result from large
  • Retrieving data from large
  • Generating reports from large
  • Extraction of hidden predictive information from large
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  • roi
  • time complexity
  • space complexity
  • All of the above
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  • marketing strategies
  • inventory management
  • sales promotion strategies
  • All of the above
Download Free : Data Mining MCQ PDF
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  • predictive
  • imperative
  • interrogative
  • retrospective
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  • Deductive learning
  • Data driven discovery.
  • Exploratory data analysis
  • All of the above
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  • operational.
  • data mining
  • informational
  • data warehouse
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  • To confirm that data exists.
  • To create a new data warehouse
  • To analyze data for expected relationships
  • To explain some observed event or condition
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  • cost-sensitive
  • work-sensitive
  • time-sensitive
  • technical-sensitive
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  • recovery
  • data pruning
  • data cleaning
  • data cleansing.
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  • intra-entry data mart.
  • inter-entry data mart.
  • dependent data marts.
  • None of the above
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  • Clustering
  • Regression
  • Summarization
  • Association rules
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  • Regression
  • Regression
  • Association rules.
  • Sequence discovery.
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  • Noisy data
  • Missing data
  • Changing data
  • Irrelevant data
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  • clustering
  • associations
  • classification
  • sequential analysis
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  • mono layer perception.
  • many layer perception
  • multi layer perception
  • None of the above
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  • Toda et al.
  • Simon et al
  • Steve et al.
  • Agrawal et al
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