Data Mining Trivia Quiz

50 Questions | Attempts: 14124

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Data Mining Trivia Quiz - Quiz

Do you know everything about data mining? You can take up this data mining trivia quiz to check your knowledge on the same. Living in a world run and managed using soft technology, data has become an essential part of human existence. The quantity of data generated and stored every day makes it hard to retrieve when needed. How much do you know about data mining? The quiz results will show how well you know about this topic. All the best!


Questions and Answers
  • 1. 
    The data that helps to identify substructures is considered to be ___________ data
  • 2. 
    What are some technique and approach to the Feature Subset Selection:
    • A. 

      Dictionary Hack Approach

    • B. 

      Dynamic brute force approach

    • C. 

      Brute force approach

    • D. 

      Embedded approach

    • E. 

      Filter approach

    • F. 

      Wrapper approaches:

  • 3. 
    Graph data set consists of: 
    • A. 

      World Wide Web, Molecular Structures

    • B. 

      Spatial Data, Temporal Data, Sequential Data, Genetic Sequence Data

    • C. 

      Generic Data, Inferential Data, Continuous Data

    • D. 

      Data Matrix, Document Data, Transaction Data

  • 4. 
    An ordered data set consists of: 
    • A. 

      World Wide Web, Molecular Structures

    • B. 

      Spatial Data, Temporal Data, Sequential Data, Genetic Sequence Data

    • C. 

      Generic Data, Inferential Data, Continuous Data

    • D. 

      Data Matrix, Document Data, Transaction Data

  • 5. 
    [Blank] data set may include data objects that are duplicates or almost duplicates of one another.
  • 6. 
    Important Characteristics of Structured Data are:
    • A. 

      Generality

    • B. 

      Dimensionality

    • C. 

      Resolution

    • D. 

      Spacial

    • E. 

      Sparsity

  • 7. 
    Which seven of these are part of Data Preprocessing?
    • A. 

      Aggregation

    • B. 

      Distortion

    • C. 

      Sequel Ordering

    • D. 

      Sampling

    • E. 

      Dimensionality Reduction

    • F. 

      Bias Resolution

    • G. 

      Feature subset selection

    • H. 

      Feature creation

    • I. 

      Rendering Objects

    • J. 

      Discretization and Binarization

    • K. 

      Pixelation

    • L. 

      Attribute Transformation

    • M. 

      Attribute Regression

  • 8. 
    What are the methodologies of Feature Creation?
    • A. 

      Brute Force approach

    • B. 

      Feature Extraction

    • C. 

      Mapping Data to New Space

    • D. 

      Sparsity Feature

    • E. 

      Feature Construction

  • 9. 
    Which of the following is NOT an example of sampling?
    • A. 

      It is the main technique employed for data selection

    • B. 

      Statisticians sample because obtaining the entire set of data of interest is too expensive or time-consuming.

    • C. 

      It is used in data mining because processing the entire set of data or interests is too expensive or time-consuming.

    • D. 

      Because it is easier and viable to use

  • 10. 
    The type of an Ordinal attribute depends on which of the following properties:
    • A. 

      Distinctness & order

    • B. 

      Distinctness, order & addition

    • C. 

      Distinctness

    • D. 

      All of the above

  • 11. 
    __________ is a systematic variation of Measurements from the quantity being measured.
  • 12. 
    [Blank]__ data is a data that consists of a collection of records, each of which consists of a fixed set of attributes
  • 13. 
    The type of an Interval attribute depends on which of the following properties:
    • A. 

      Distinctness & order

    • B. 

      Distinctness, order & addition

    • C. 

      Distinctness

    • D. 

      All of the above

  • 14. 
    ____________ data is sequences of transactions or genomic sequence data
  • 15. 
    ________ is the closeness of measurements to the true value of the quantity being measured.
  • 16. 
    An _____ is a property or characteristic of an object. Example: eye color of a person, temperature, etc.
  • 17. 
    Data Matrix is:
    • A. 

      If data objects have the same fixed set of numeric attributes, then the data objects can be thought of as points in a multi-dimensional space, where each dimension represents a distinct attribute.

    • B. 

      Such data set can be represented by an m by n matrix, where there are m rows, one for each object, and n columns, one for each attribute.

    • C. 

      Neither A nor B

    • D. 

      Both A and B

  • 18. 
    _________ is the closeness of repeated measurements (of the same quantity) to other measurements.
  • 19. 
    Proximity refers to a ____________ and ___________
  • 20. 
    A collection of attributes describe an object?
    • A. 

      True

    • B. 

      False

  • 21. 
    The type of a Ratio attribute depends on which of the following properties:
    • A. 

      Distinctness & order

    • B. 

      Distinctness, order & addition

    • C. 

      Distinctness

    • D. 

      All 4 properties

  • 22. 
    [Blank]__ refers to the modification of original values, such as distortion of a person's voice when talking on a poor phone and "snow" on the television screen.
  • 23. 
    _______ values are numbers or symbols assigned to an attribute
  • 24. 
    _______ Attribute has only a finite or countably infinite set of values, often represented as integer variables, Example: zip codes, counts, or the set of words in a collection of documents
  • 25. 
    In Curse of Dimensionality, when dimensionality increases, data becomes increasingly sparse in the space that it occupies.
    • A. 

      True

    • B. 

      False

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