Question 1

Which of the following is not true of data mining?
A) Data mining has seen explosive growth in the area of customer relationship management.
B) Data mining is a business solution.
C) Data mining is a technology.
D) None of the above are true.

Accepted Answer

The answer of Which of the following is not true...

Question 2

The set of activities used to find new, hidden, or unexpected patterns in data is referred to as:
A) data warehousing.
B) data mining.
C) data transformation.
D) data aggregation.

Accepted Answer

Data mining involves analyzing large sets of data to discover patterns, trends, and relationships that might not be immediately apparent, which can be used for decision-making and predicting future trends.

Question 3

Which of the following is a reason for the growth in popularity of data mining?
A) Increased volume of data
B) Increased awareness of the inadequacy of the human brain to process multifactorial dependencies or correlations
C) Increased affordability of machine learning
D) All of the above.

Accepted Answer

All of the choices listed contribute to the growth in popularity of data mining. The increased volume of data necessitates more sophisticated methods for analysis, the recognition of the human brain's limitations in processing complex data encourages the use of automated systems, and the increased affordability of machine learning technologies makes these tools accessible to a wider range of users.

Question 4

The term _________ has been generally agreed to represent the broad category of software technology that enables decision makers to conduct multidimensional analysis of consolidated enterprise data.
A) OLAP
B) MOLAP
C) ROLAP
D) None of the above.

Accepted Answer

OLAP (Online Analytical Processing) is the term widely recognized for representing the category of software technology that allows decision makers to perform multidimensional analysis of consolidated data from across the enterprise.

Question 5

____________organizes and analyzes data as an n-dimensional cube. The cube can be thought of as a common spreadsheet with two extensions: (1) support for multiple dimensions and (2) support for multiple concurrent users.
A) OLAP
B) MOLAP
C) ROLAP
D) None of the above.

Accepted Answer

The answer of ____________organizes and analyzes data as an n-dimensional...

Question 6

In _____________, the multidimensional database server is replaced with a large relational database server. This "super" relational database will contain both detailed and summarized data thus allowing for "drill down" techniques to be applied to the data sets.
A) OLAP
B) MOLAP
C) ROLAP
D) None of the above.

Accepted Answer

ROLAP (Relational Online Analytical Processing) involves using a relational database to store and manage warehouse data, and it supports the use of "drill down" techniques on detailed and summarized data.

Question 7

In a ________________, the data are stored as a multidimensional array where each cell in the array represents the intersection of all of the dimensions. Using this approach, any number of dimensions may be analyzed simultaneously and any number of multidimensional views of the data can be created.
A) hyperion cube
B) hypercube
C) stochastic cube
D) correlation cube

Accepted Answer

The correct term is "hypercube," which refers to a multidimensional array used in data storage and analysis, allowing for simultaneous analysis of multiple dimensions and the creation of multidimensional views of the data.

Question 8

Which term is used to refer to a basic database operation that links rows of two or more tables by one or more columns in each table?
A) n-cube analysis
B) table link
C) table join
D) None of the above.

Accepted Answer

The term "table join" refers to a fundamental database operation that connects rows from two or more tables based on common columns between these tables.

Question 9

Which of the following is not considered one of the four major categories of processing algorithms and rule approaches?
A) Classification
B) Association
C) Sequence
D) Principal components analysis

Accepted Answer

Principal components analysis is a technique used for dimensionality reduction rather than a category of processing algorithms or rule approaches like Classification, Association, or Sequence.

Question 10

Which data mining technique utilizes linkage analysis to search operational transactions for patterns with a high probability of repetition?
A) Association
B) Cluster
C) Sequence
D) Principal components analysis

Accepted Answer

Association rule mining is the technique that searches for relationships between variables in large databases. It is commonly used for market basket analysis to find products that frequently co-occur in transactions, which is a form of linkage analysis to discover patterns that are likely to be repeated.

Question 11

There are some cases where it is difficult or impossible to define the parameters of a class of data to be analyzed. In these cases, _____________ methods can be used to create partitions so that all members of each set are similar according to some metric or set of metrics thus creating a set of objects grouped together by virtue of their similarity or proximity to each other.
A) association
B) linkage analysis
C) sequencing
D) clustering

Accepted Answer

Clustering methods are used to create partitions in a dataset when it's difficult to define parameters, grouping objects based on their similarity or proximity to each other.

Question 12

___________________ methods are used to relate events in time, such as the prediction of interest rate fluctuations or stock performance, based on a series of preceding events. Through this analysis, various hidden trends can be discovered that are often highly predictive of future events.
A) Association
B) Linkage analysis
C) Sequencing
D) Clustering

Accepted Answer

Sequencing methods are used to analyze and predict events based on a series of preceding events, identifying trends that can forecast future occurrences.

Question 13

Which of the following is not a data mining technology?
A) Statistical analysis
B) Neural networks
C) Decision trees
D) All of the above are data mining technologies.

Accepted Answer

All of the options listed (Statistical analysis, Neural networks, Decision trees) are indeed data mining technologies. Data mining involves exploring and analyzing large blocks of information to glean meaningful patterns and trends. It can involve the use of various techniques including, but not limited to, statistical analysis, neural networks, and decision trees.

Question 14

A common example of the use of association methods where a retailer can mine the data generated by a point-of-sale system, such as the price scanner you are familiar with at the grocery store is referred to as:
A) sequencing.
B) linkage analysis.
C) clustering.
D) market basket analysis.

Accepted Answer

Market basket analysis is a data mining technique used by retailers to discover associations between items. It analyzes the items that customers buy together to find patterns and correlations in large datasets, such as those generated by point-of-sale systems in grocery stores. This information can help in product placement, promotions, and inventory management.

Question 15

The process by which numerical data is converted into meaningful images is referred to as:
A) data mining.
B) data warehousing.
C) data visualization.
D) data aggregation.

Accepted Answer

Data visualization is the process of converting numerical data into graphical or pictorial format to make the information comprehensible and actionable for users, allowing for easier identification of patterns, trends, and outliers within large data sets.

Question 16

Which of the following is not an advantage of market basket analysis?
A) Identifying which products sell together can help manage inventory.
B) It is more preferable to marketers to market to existing customers.
C) Market basket analysis can sometimes produce results that are due to prior marketing campaigns.
D) All of the above are advantages.

Accepted Answer

Market basket analysis is primarily used to understand the purchase behavior of customers and find patterns in product purchases. While it helps in inventory management and targeting existing customers effectively, stating that results being due to prior marketing campaigns is not an advantage. It actually suggests a limitation or a confounding factor in interpreting the analysis results, as it indicates that the patterns found might not solely be due to natural customer preferences but could be influenced by external marketing efforts.

Question 17

An orderly hierarchy of items and item categories that divides each item into a basket analysis is referred to as a:
A) cluster analysis.
B) taxonomy.
C) multi dimensional market basket analysis.
D) None of the above.

Accepted Answer

A taxonomy is a classification scheme that arranges the elements of a particular field into categories and subcategories based on their relationships and attributes, fitting the description of an orderly hierarchy of items and item categories.

Question 18

Which of the following is a limitation of data mining?
A) Identification of missing data
B) Data noise
C) Missing values
D) All of the above.

Accepted Answer

The answer of Which of the following is a limitation...

Question 19

What are Codd's twelve rules for OLAP?

Accepted Answer

The answer of What are Codd's twelve rules for OLAP?...

Quiz 11: Data Mining and Data Visualization

Which of the following is not true of data mining?

The set of activities used to find new, hidden, or unexpected patterns in data is referred to as:

Which of the following is a reason for the growth in popularity of data mining?

The term _________ has been generally agreed to represent the broad category of software technology that enables decision makers to conduct multidimensional analysis of consolidated enterprise data.

____________organizes and analyzes data as an n-dimensional cube. The cube can be thought of as a common spreadsheet with two extensions: (1) support for multiple dimensions and (2) support for multiple concurrent users.

In _____________, the multidimensional database server is replaced with a large relational database server. This "super" relational database will contain both detailed and summarized data thus allowing for "drill down" techniques to be applied to the data sets.

In a ________________, the data are stored as a multidimensional array where each cell in the array represents the intersection of all of the dimensions. Using this approach, any number of dimensions may be analyzed simultaneously and any number of multidimensional views of the data can be created.

Which term is used to refer to a basic database operation that links rows of two or more tables by one or more columns in each table?

Which of the following is not considered one of the four major categories of processing algorithms and rule approaches?

Which data mining technique utilizes linkage analysis to search operational transactions for patterns with a high probability of repetition?

___________________ methods are used to relate events in time, such as the prediction of interest rate fluctuations or stock performance, based on a series of preceding events. Through this analysis, various hidden trends can be discovered that are often highly predictive of future events.

Which of the following is not a data mining technology?

A common example of the use of association methods where a retailer can mine the data generated by a point-of-sale system, such as the price scanner you are familiar with at the grocery store is referred to as:

The process by which numerical data is converted into meaningful images is referred to as:

Which of the following is not an advantage of market basket analysis?

An orderly hierarchy of items and item categories that divides each item into a basket analysis is referred to as a:

Which of the following is a limitation of data mining?

What are Codd's twelve rules for OLAP?