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Welcome to the Data Mining Basics MCQs Page

Dive deep into the fascinating world of Data Mining Basics with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Data Mining Basics, a crucial aspect of Data Warehousing and OLAP. In this section, you will encounter a diverse range of MCQs that cover various aspects of Data Mining Basics, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Data Warehousing and OLAP.

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Check out the MCQs below to embark on an enriching journey through Data Mining Basics. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Data Warehousing and OLAP.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Data Mining Basics. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Data Mining Basics MCQs | Page 7 of 13

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Q61.
What is the purpose of associations discovery in link analysis?

a.

Discovering patterns where one set of items follows another specific set

b.

Discovering affinities between items and finding combinations where the presence of one item suggests the presence of another

c.

Finding similar sequential price movements of stock

d.

Tracing the decision paths in decision trees

Discuss
Answer: (b).Discovering affinities between items and finding combinations where the presence of one item suggests the presence of another Explanation:The purpose of associations discovery in link analysis is to find affinities between items and discover combinations where the presence of one item suggests the presence of another.
Q62.
What are association rules in link analysis?

a.

Decision rules that determine the classification of records

b.

Rules with high support and confidence factor values indicating the strength of the association between items

c.

Rules governing the sequential order of events

d.

Rules for pruning the branches of decision trees

Discuss
Answer: (b).Rules with high support and confidence factor values indicating the strength of the association between items Explanation:Association rules in link analysis are rules with high support and confidence factor values indicating the strength of the association between items.
Q63.
What is the challenge associated with scaling in associations discovery?

a.

The need for a large dataset

b.

The intuitive interpretation of factors

c.

The counting of combinations as the dataset is read repeatedly

d.

The requirement for date and time data items

Discuss
Answer: (c).The counting of combinations as the dataset is read repeatedly Explanation:The challenge associated with scaling in associations discovery is the counting of combinations as the dataset is read repeatedly each time new dimensions are added.
Q64.
In sequential pattern discovery, what role does the time element play?

a.

It determines the support factor.

b.

It indicates the relevance of the association.

c.

It helps discover patterns where one set of items follows another specific set.

d.

It enables discovery of sequential patterns, with date and time as data items.

Discuss
Answer: (d).It enables discovery of sequential patterns, with date and time as data items. Explanation:In sequential pattern discovery, the time element plays a role by enabling the discovery of sequential patterns, with date and time as data items.
Q65.
What is the basic unit of an artificial neural network?

a.

Link

b.

Neuron

c.

Node

d.

Predictor

Discuss
Answer: (c).Node Explanation:The basic unit of an artificial neural network is a node.
Q66.
What is the role of weights in a neural network?

a.

They represent the input variables.

b.

They condition the predictors properly.

c.

They connect neurons in the brain.

d.

They are the output nodes.

Discuss
Answer: (b).They condition the predictors properly. Explanation:In a neural network, weights may be applied to the predictors to condition them properly.
Q67.
What is the other main structure of the neural network model, apart from nodes?

a.

Predictors

b.

Hidden layers

c.

Link

d.

Input nodes

Discuss
Answer: (c).Link Explanation:The other main structure of the neural network model is the link, which corresponds to the connection between neurons in the brain.
Q68.
In genetic algorithms, what does the process of selection, cross-over, and mutation aim to achieve?

a.

To produce identical models in each generation

b.

To maximize profits in a promotional mailing

c.

To promote the survival of the fittest models over successive generations

d.

To apply the principles of neural networks to data mining

Discuss
Answer: (c).To promote the survival of the fittest models over successive generations Explanation:In genetic algorithms, the process of selection, cross-over, and mutation aims to promote the survival of the fittest models over successive generations.
Q69.
How does the genetic algorithm evolve successive generations of models?

a.

By replicating the same models in each generation

b.

By applying identical traits to all models

c.

By competing models and inheriting traits from previous generations

d.

By eliminating all but one model in each generation

Discuss
Answer: (c).By competing models and inheriting traits from previous generations Explanation:The genetic algorithm evolves successive generations of models by competing models and inheriting traits from previous generations.
Q70.
What is the foundational data source for data mining processes?

a.

Operational systems

b.

Data warehouse

c.

OLAP systems

d.

ETL functions

Discuss
Answer: (b).Data warehouse Explanation:The foundational data source for data mining processes is the data warehouse.
Page 7 of 13

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