Data Mining and Business Intelligence 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

  • (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 algorithm

  • (A) DBSCAN
  • (B) BIRCH
  • (C) PAM
  • (D) CURE

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