This set of Data Mining Multiple Choice Questions & Answers (MCQs) focuses on “What Kind of Patterns can be Mined”.
1. Pick the wrong data mining functionality among the given data mining functionalities.
a) Class Description
b) Object Description
c) Classification
d) Clustering
View Answer
Explanation: There are 5 data mining functionalities. They are class/concept description, Mining Frequent Patterns: associations and correlations, Classification and Regression, Clustering and Outlier analysis.
2. Given below are the descriptive data mining functionalities. Pick the functionality which does not come under the predictive task of the data mining tasks.
a) Associations
b) Classification
c) Cluster Analysis
d) Outlier Analysis
View Answer
Explanation: Data Mining Functionalities are classified into 2. Predictive and Descriptive Tasks. Predictive Tasks: Classification, prediction, Evolution Analysis. Descriptive Tasks: Class/Concept Description, Associations and Correlations, Cluster Analysis, Outlier Analysis.
3. Class/Concept Description has _____ number of tasks.
a) 2
b) 4
c) 3
d) 8
View Answer
Explanation: Class/Concept Description has 2 tasks. They are Characterization and Summarization. Characterization is the general summarization of general features of the Target Class data. The data is generally from the database in the form of a query.
4. Characterization is an application of one of the Data Warehouse’s Operation. Which among the listed below is that operation?
a) Drill down
b) Roll Up
c) Pivot
d) Slice
View Answer
Explanation: Characterization also known as Summarization is a roll up operation. Discrimination is the application of Drill down Operation; Pivot is the operation of viewing the data in various dimensions. Slice is the other operations which are generally used in Data Mining.
5. Which among the following tasks have Training data available for Mining?
a) Cluster Analysis
b) Outlier Analysis
c) Associations and Correlations
d) Prediction
View Answer
Explanation: All Predictive tasks have Training data and Prediction is a Predictive Task. Cluster Analysis, Outlier Analysis, Associations are Descriptive tasks and Class Label are not Present for them. Class labels are also known as training data.
6. Data Characterization can be done by 2 operations. One of them is roll up operation. The other one is _____
a) Drill down
b) Dice
c) Attribute oriented induction
d) Slice
View Answer
Explanation: attribute-oriented induction technique can be used to perform data generalization and characterization without step-by-step user interaction. Data Cube based OLAP operation is used to perform user controlled data summarization.
7. Which one is the proper definition for data discrimination?
a) Sorting the data as per the attribute names
b) Sorting the data according to the target class data
c) Discriminating between target class and contrasting classes
d) Discriminating the data as per the type of Database from which it is collected.
View Answer
Explanation: Data discrimination is a comparison of the general features of the target class data objects against the general features of objects from one or multiple contrasting classes. The target and contrasting classes can be specified by a user, and the corresponding data objects can be retrieved through database queries.
8. There are two ways in which one can represent the results of Data Discrimination. They are Comparative measures and Discriminative rules.
a) False
b) True
View Answer
Explanation: The forms of output presentation for the output of Data Discrimination are similar to those for characteristic descriptions; discrimination descriptions should include comparative measures that to distinguish between the target and contrasting classes. Discrimination descriptions expressed in the form of rules are referred to as discriminantrules.
9. Which among the following cannot be categorized into frequent pattern?
a) Items which occur frequently in the document/ data base
b) Dimensions in which the data is categorized into frequently occurs
c) Patterns which frequently occur in the data base
d) Item sets and patterns that frequently occur in the database
View Answer
Explanation: Frequent Patterns are patterns that occur frequently in data. There are 3 different kinds of frequent patterns, including frequent item sets, frequent subsequences (also known as sequential patterns), and frequent substructures.
10. What is a frequent item set?
a) Items which frequently occur together
b) Items which frequently don’t occur together
c) Patterns which generally occur together
d) Patterns which generally doesn’t occur together
View Answer
Explanation: A frequent item set typically refers to a set of items that often appear together in a transactional data set—for example, milk and bread, which are frequently bought together in a grocery store by many customers.
11. A substructure that occurs frequently is called structured pattern.
a) True
b) False
View Answer
Explanation: A substructure can refer to different structural forms (e.g., graphs, trees, or lattices) that may be combined with itemsets or subsequences. If a substructure occurs frequently, it is called a (frequent) structured pattern.
12. Association Analysis is shown in the form of a rule and is supported by 2 of the components. What are they?
a) Support and Confidence
b) Threshold and Support
c) Confidence and Threshold
d) Confidence and Correlation
View Answer
Explanation: Association analysis is assured by Support and Confidence.
Ex: buys(X,“computer”)⇒buys(X,“software”) [support=1%,confidence=50%], where X is a variable representing a customer. A confidence, or certainty, of 50% means that if a customer buys a computer, there is a 50% chance that she will buy software as well. A 1% support means that 1% of all the transactions under analysis show that computer and software are purchased together.
13. Classification is the process of finding a model that describes and distinguishes data classes or concepts .The output of the classification can be categorized into rules, decision trees, etc. Into how many possible ways are the outputs classified?
a) 4
b) 5
c) 6
d) 3
View Answer
Explanation: The derived model may be represented in various forms, such as Classification rules (i.e., IF-THEN rules), decision trees, mathematical formulae, or neural networks.
14. A decision tree is a flowchart like structure and has nodes and branches. What does branches and nodes represent, respectively?
a) Attribute value and outcome of the test
b) Outcome of the test and attribute value
c) Classes and Class Description
d) Class Description and Classes
View Answer
Explanation: A decision tree is a flowchart like structure where each node denotes a test on attribute value and each branch represents an outcome of the test and tree leaves represents classes and class distribution.
15. Given below is the output of the Frequent Patterns. What is the form in which the output is represented?
a) Decision Tree
b) Flow Chart
c) Association rules
d) If-then rules
View Answer
Explanation: Decision trees and flowcharts should have rhombus, rectangles, circles etc in their diagrams to represent the output. Association rules are always assured by support and Confidence. The above rule is an example of if-then rule.
Sanfoundry Global Education & Learning Series – Data Mining.
To practice all areas of Data Mining, here is complete set of Multiple Choice Questions and Answers.
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