Page 92 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Data Warehousing and Data Mining

notes 4.13 self assessment

Fill in the blanks:

1. Bayesian classifiers are ...................... classifiers.
2. Bayesian classification is based on ...................... theorem
3. ...................... classifiers assume that the effect of an attribute value on a given class is
independent of the values of the other attributes.

4. Bayesian belief networks are ...................... models.
5. In theory ...................... classifiers have the minimum error rate in comparison to all other
classifiers.
6. ...................... is a data mining technique used to predict group membership for data
instances.
7. The learning of the model is ...................... if it is told to which class each training sample
belongs.
8. ...................... refers to the ability of the model to correctly predict the class label of new or
previously unseen data.

9. A ...................... is a flow-chart-like tree structure.
10. The ...................... measure is used to select the test attribute at each node in the tree.

4.14 review Questions

1. What do you mean by classification in data mining? Write down the applications of
classification in business.
2. What do you mean by prediction? Write down the applications of classification in
business.
3. What is the difference between the classification and predicate?

4. Discuss the issues regarding the classification and predicate.
5. Differentiate between the supervised and unsupervised learning.
6. What do you mean by data cleaning?
7. Write down the criteria to compare and evaluate the classification and prediction
methods.
8. What is a decision tree? Explain with the help of a suitable example.
9. Write down the basic algorithm for decision learning trees.
10. How effective are Bayesian classifiers?

11. Write short notes on the followings:
(a) Bayesian classification
(b) Bayes theorem
(c) Naive Bayesian classification

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