Data Mining & Data Ware House

Data Mining & Data Ware House
61. Machine learning is
  • An algorithm that can learn
  • A sub-discipline of computer science that deals with the design and implementation of learning algorithms.
  • An approach that abstracts from the actual strategy of an individual algorithm and can therefore be applied to any other form of machine learning.
  • None of these
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62. Inductive  logic programming is
  • A class of learning algorithm that try to derive a prolog program from examples
  • A table with n independent attributes can be seen as an n- dimensionlal space
  • A prediction made using an extremely simpl method, such as always predicting the same output.
  • None of these
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63. Multi-dimensional knowledge is
  • A class of learning algorithms that try to derive a Prolog program from examples
  • A table with n independent attributes can be seen as an n- dimensional space
  • A pediction made using an extremely simple method, such as always predicting the same output.
  • None of these
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64. Naive prediction is
  • A class of learning algorithms that try to derive a Prolog program from examples
  • A table with n independent attributes can be seen as an n -dimensional space
  • A prediction made using an extremely simple method, such asalways predicting the same output
  • None of these
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65. Knowledge is referred to
  • Non-trivial extraction of implicit previously unknown and potentially useful information from dat(A)
  • Set of columns in a database table that can be used to identify each record within this table uniquely
  • collection of interesting and useful patterns in a database
  • none of these
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66. Node is
  • A component of network
  • In the context of KDD and data mining this refers to random errors in a database table
  • One of the defining aspects of a data warehouse
  • None of these
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67. Node is
  • A component of a network
  • In the context of KDD and data mining, this refers to random errors in a database table
  • One of the defining aspects of a d ata warehouse
  • None of these
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68. Node is
  • A component of a network
  • In the context of KDD and data mining, this refers to random errors in a database table
  • One of the defining aspects of a data warehouse
  • None of these
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69. Projection pursuit is
  • The result of the application of a theory or a rule in a specific case
  • One of several possible enters within a database table that is chosen by the designer as the primary means of accessing the data in the table.
  • Discipline in statistics that studies ways to find the most interesting projections of multi-dimensional spaces
  • None of these
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70. Statistical significance is
  • The science of collecting, organizing, and applying numberical facts
  • Measure of the probabiity that a certain hypothesis is incorrect given certain observations.
  • One of the defining aspects of a data warehouse, which is specially built around all the existing applications of the operational dat(A)
  • None of these
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