Data Mining and Data Warehouse MCQs with answers Page - 18

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Q. The non-root node of item-prefix-tree consists of ________ fields.

  • (A) two.
  • (B) three.
  • (C) four.
  • (D) five.

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Q. The frequent-item-header-table consists of __________ fields.

  • (A) only one.
  • (B) two.
  • (C) three.
  • (D) four.

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Q. The goal of _____ is to discover both the dense and sparse regions of a data set.

  • (A) association rule.
  • (B) classification.
  • (C) clustering.
  • (D) genetic algorithm.

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Q. Which of the following is a clustering algorithm?

  • (A) a priori.
  • (B) clara.
  • (C) pincer-search.
  • (D) fp-growth.

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Q. _______ clustering technique start with as many clusters as there are records, with each cluster having only one record.

  • (A) agglomerative.
  • (B) divisive.
  • (C) partition.
  • (D) numeric.

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Q. __________ clustering techniques starts with all records in one cluster and then try to split that cluster into small pieces.

  • (A) agglomerative.
  • (B) divisive.
  • (C) partition.
  • (D) numeric.

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Q. Which of the following is a data set in the popular UCI machine-learning repository?

  • (A) clara.
  • (B) cactus.
  • (C) stirr.
  • (D) mushroom.

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Q. In ________ algorithm each cluster is represented by the center of gravity of the cluster.

  • (A) k-medoid.
  • (B) k-means.
  • (C) stirr.
  • (D) rock.

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Q. In ___________ each cluster is represented by one of the objects of the cluster located near the center.

  • (A) k-medoid.
  • (B) k-means.
  • (C) stirr.
  • (D) rock.

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Q. Pick out a k-medoid algoithm.

  • (A) dbscan.
  • (B) birch.
  • (C) pam.
  • (D) cure.

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