Data Mining and Knowledge Discovery Exam
Review
Course Introduction
Data Mining and Knowledge Discovery explores the methodologies, algorithms, and tools used to extract meaningful patterns and insights from large volumes of complex data. The course covers key topics such as data preprocessing, classification, clustering, association rule mining, anomaly detection, and dimensionality reduction. Students will learn theoretical foundations as well as practical applications of data mining techniques in various domains, gaining proficiency in using software tools and programming languages to analyze datasets. Throughout the course, there will be an emphasis on evaluating the quality and usefulness of discovered knowledge, ethical considerations, and dealing with real-world challenges such as data quality, scalability, and interpretability.
Recommended Textbook
Data Mining A Tutorial Based Primer 1st Edition by Richard Roiger
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14 Chapters
167 Verified Questions
167 Flashcards
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Chapter 1: Data Mining: a First View
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22 Verified Questions
22 Flashcards
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Sample Questions
Q1) Which of the following is not a characteristic of a data warehouse?
A) contains historical data
B) designed for decision support
C) stores data in normalized tables
D) promotes data redundancy
Answer: C
Q2) Determine whether a credit card transaction is valid or fraudulent.
A)supervised learning
B)unsupervised clustering
C)data query
Answer: A
Q3) The process of forming general concept definitions from examples of concepts to be learned.
A) deduction
B) abduction
C) induction
D) conjunction
Answer: C
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Chapter 2: Data Mining: a Closer Look
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16 Verified Questions
16 Flashcards
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Sample Questions
Q1) Unlike traditional production rules, association rules
A) allow the same variable to be an input attribute in one rule and an output attribute in another rule.
B) allow more than one input attribute in a single rule.
C) require input attributes to take on numeric values.
D) require each rule to have exactly one categorical output attribute.
Answer: A
Q2) How many class 2 instances are in the dataset?
Answer: 23
Q3) Another name for an output attribute.
A) predictive variable
B) independent variable
C) estimated variable
D) dependent variable
Answer: D
Q4) Compute the lift for Model Y.
Answer: 8/7
Q5) How many instances were classified as an accept by Model X?
Answer: 35
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Chapter 3: Basic Data Mining Techniques
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13 Flashcards
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Sample Questions
Q1) The computational complexity as well as the explanation offered by a genetic algorithm is largely determined by the
A) fitness function
B) techniques used for crossover and mutation
C) training data
D) population of elements
Answer: A
Q2) This approach is best when we are interested in finding all possible interactions among a set of attributes.
A) decision tree
B) association rules
C) K-Means algorithm
D) genetic learning Computational Questions
Answer: B
Q3) An evolutionary approach to data mining.
A) backpropagation learning
B) genetic learning
C) decision tree learning
D) linear regression
Answer: B
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Chapter 4: An Excel-Based Data Mining Tool
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12 Flashcards
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Sample Questions
Q1) ESX represents the overall similarity of the exemplars contained in an individual class by computing a ____ score.
A) class resemblance
B) class predictability
C) class predictiveness
D) typicality
Q2) Suppose that the predictiveness score for risk factor = medium risk is 0.50. How many domain instances have a value of medium risk for the risk factor attribute?
A) 10
B) 20
C) 30
D) 40
Q3) What is the predictability score for the attribute value medium risk?
A) 0.10
B) 0.20
C) 0.25
D) 0.50
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Chapter 5: Knowledge Discovery in Databases
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10 Flashcards
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Sample Questions
Q1) This data transformation technique works well when minimum and maximum values for a real-valued attribute are known.
A) min-max normalization
B) decimal scaling
C) z-score normalization
D) logarithmic normalization
Q2) Attibutes may be eliminated from the target dataset during this step of the KDD process.
A) creating a target dataset
B) data preprocessing
C) data transformation
D) data mining
Q3) A data normalization technique for real-valued attributes that divides each numerical value by the same power of 10.
A) min-max normalization
B) z-score normalization
C) decimal scaling
D) decimal smoothing
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Chapter 6: The Data Warehouse
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Sample Questions
Q1) A mapping that allows the attributes of an OLAP cube to be viewed from varying levels of detail.
A) reflection
B) concept hierarchy
C) rotation
D) semantic network
Q2) Which of the following is not a characteristic of a data warehouse?
A) contains nonvolatile data
B) is subject oriented
C) supports data processing, collection and management
D) stores data to be reported on, analyzed and tested
Q3) A variation of the star schema that allows more than one central fact table.
A) snowflake schema
B) linked strar schema
C) distributed star schema
D) constellation schema
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Chapter 7: Formal Evaluation Techniques
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Sample Questions
Q1) Bootstrapping allows us to
A) choose the same training instance several times.
B) choose the same test set instance several times.
C) build models with alternative subsets of the training data several times.
D) test a model with alternative subsets of the test data several times.
Q2) The correlation coefficient for two real-valued attributes is -0.85. What does this value tell you?
A) The attributes are not linearly related.
B) As the value of one attribute increases the value of the second attribute also increases.
C) As the value of one attribute decreases the value of the second attribute increases.
D) The attributes show a curvilinear relationship.
Q3) The standard error is defined as the square root of this computation.
A) The sample variance divided by the total number of sample instances.
B) The population variance divided by the total number of sample instances.
C) The sample variance divided by the sample mean.
D) The population variance divided by the sample mean.
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Chapter 8: Neural Networks
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Sample Questions
Q1) Epochs represent the total number of
A) input layer nodes.
B) passes of the training data through the network.
C) network nodes.
D) passes of the test data through the network.
Q2) A two-layered neural network used for unsupervised clustering.
A) backpropagation network
B) Kohonen network
C) perceptron network
D) aggomerative network
Q3) Which one of the following is not a major strength of the neural network approach?
A) Neural networks work well with datasets containing noisy data.
B) Neural networks can be used for both supervised learning and unsupervised clustering.
C) Neural network learning algorithms are guaranteed to converge to an optimal solution.
D) Neural networks can be used for applications that require a time element to be included in the data.
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Chapter 9: Building Neural Networks With Ida
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Sample Questions
Q1) This type of supervised network architecture does not contain a hidden layer.
A) backpropagation
B) perceptron
C) self-organizing map
D) genetic
Q2) Two classes each of which is represented by the same pair of numeric attributes are linearly separable if
A) at least one of the pairs of attributes shows a curvilinear relationship between the classes.
B) at least one of the pairs of attributes shows a high positive correlation between the classes.
C) at least one of the pairs of attributes shows a high positive correlation between the classes
D) a straight line partitions the instances of the two classes
Q3) The test set accuracy of a backpropagation neural network can often be improved by
A) increasing the number of epochs used to train the network.
B) decreasing the number of hidden layer nodes.
C) increasing the learning rate.
D) decreasing the number of hidden layers.
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Chapter 10: Statistical Techniques
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Sample Questions
Q1) With Bayes classifier, missing data items are
A) treated as equal compares.
B) treated as unequal compares.
C) replaced with a default value.
D) ignored.
Q2) This technique associates a conditional probability value with each data instance.
A) linear regression
B) logistic regression
C) simple regression
D) multiple linear regression
Q3) The leaf nodes of a model tree are
A) averages of numeric output attribute values.
B) nonlinear regression equations.
C) linear regression equations.
D) sums of numeric output attribute values.
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Chapter 11: Specialized Techniques
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Sample Questions
Q1) Usage profiles for Web-based personalization contain several
A) pageviews
B) clickstreams
C) cookies
D) session files
Q2) These can be used to help select a best subset of training data.
A) domain resemblance scores
B) class resemblance scores
C) instance typicality scores
D) standard deviation scores
Q3) A data file that contains session information.
A) cookie
B) pageview
C) page frame
D) common log
Q4) The training phase of a textual data mining process involves A) removing common words from a dictionary.
B) creating an attribute dictionary.
C) determining whether a document is about the topic under investigation.
D) modifying an initially created attribute dictionary.
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Chapter 12: Rule-Based Systems
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15 Verified Questions
15 Flashcards
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Sample Questions
Q1) Knowledge about knowledge is known as
A) metaknowledge
B) class knowledge
C) structured knowledge
D) classified knowledge
Q2) Construct a goal tree using the following production rules. Assume the goal is g.
Q3) Which of the following problems is not a good candidate for an expert systems solution?
A) bankruptcy prediction
B) determining company advertising strategies
C) solving the national debt crisis
D) providing investment strategies to avoid paying taxes
Q4) Any technique that helps limit the size of a search space.
A) top-down technique
B) conflict resolution strategy
C) bottom-up technique
D) heuristic
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Chapter 13: Managing Uncertainty in Rule-Based Systems
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Sample Questions
Q1) A car mechanic tells you that there is a 75% chance that your car will need major repair work within the next six months. This statement is an example of
A) an objective probability.
B) an experimental probability.
C) a subjective probability.
D) a fuzzy probability.
Q2) This process converts a crisp numeric value to its corresponding degree of membership within a fuzzy set.
A) composition
B) inference
C) fuzzification
D) defuzzification
Q3) Probabilities based on frequency distributions obtained through sampling.
A) objective
B) experimental
C) subjective
D) inexact
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Chapter 14: Intelligent Agents
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Sample Questions
Q1) Autonomy is an agent's ability to
A) react to a changing environment.
B) act without direct intervention from others.
C) confer with other agents.
D) react to sensory information received from the environment.
Q2) A fundamental difference between a data mining approach to problem solving and an expert systems approach is
A) the output of an expert system is a set of rules and the output of a data mining technique is a decision tree.
B) a data mining technique builds a model without the aid of a human expert.
C) a model built using a data mining technique can explain how decisions are made but an expert system cannot.
D) an expert system is built using inductive learning whereas a data mining model is built using one or several deductive techniques
Q3) An agent's ability to choose its actions in the context of other agents.
A) autonomy
B) cooperation
C) adaptivity
D) coordination
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