Information Retrieval Solved
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Course Introduction
Information Retrieval is the study of methods and systems for searching, retrieving, and organizing information from large collections of unstructured or semi-structured data, such as text documents, web pages, or multimedia content. The course covers fundamental concepts including indexing, ranking algorithms, query processing, evaluation metrics, and relevance feedback. Students explore the inner workings of search engines, examine various retrieval models like Boolean, vector space, and probabilistic models, and address challenges such as scalability, efficiency, and handling of noisy or ambiguous data. Additional topics may include web search, information filtering, and personalization techniques, with practical assignments providing hands-on experience in building and evaluating information retrieval systems.
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 Flashcards
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Sample Questions
Q1) Develop a profile for credit card customers likely to carry an average monthly balance of more than $1000.00.
A)supervised learning
B)unsupervised clustering
C)data query
Answer: A
Q2) If a customer is spending more than expected, the customer's intrinsic value is ________ their actual value.
A) greater than
B) less than
C) less than or equal to D) equal to Matching Questions
Answer: B
Q3) Do meaningful attribute relationships exist in a database containing information about credit card customers?
A)supervised learning
B)unsupervised clustering
C)data query
Answer: B
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Page 3
Chapter 2: Data Mining: a Closer Look
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Sample Questions
Q1) Which statement about outliers is true?
A) Outliers should be identified and removed from a dataset.
B) Outliers should be part of the training dataset but should not be present in the test data.
C) Outliers should be part of the test dataset but should not be present in the training data.
D) The nature of the problem determines how outliers are used.
E) More than one of a,b,c or d is true.
Answer: D
Q2) Given desired class C and population P, lift is defined as
A) the probability of class C given population P divided by the probability of C given a sample taken from the population.
B) the probability of population P given a sample taken from P.
C) the probability of class C given a sample taken from population P.
D) the probability of class C given a sample taken from population P divided by the probability of C within the entire population P.
Answer: D
Q3) How many class 2 instances are in the dataset?
Answer: 23
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Page 4
Chapter 3: Basic Data Mining Techniques
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Sample Questions
Q1) Given a rule of the form IF X THEN Y, rule confidence is defined as the conditional probability that
A) Y is true when X is known to be true.
B) X is true when Y is known to be true.
C) Y is false when X is known to be false.
D) X is false when Y is known to be false.
Answer: A
Q2) The K-Means algorithm terminates when
A) a user-defined minimum value for the summation of squared error differences between instances and their corresponding cluster center is seen.
B) the cluster centers for the current iteration are identical to the cluster centers for the previous iteration.
C) the number of instances in each cluster for the current iteration is identical to the number of instances in each cluster of the previous iteration.
D) the number of clusters formed for the current iteration is identical to the number of clusters formed in the previous iteration.
Answer: B
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Chapter 4: An Excel-Based Data Mining Tool
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Sample Questions
Q1) A particular categorical attribute value has a predictiveness score of 0.5 and a predictability score of 1.0. The attribute value is
A) necessary but not sufficient for class membership.
B) sufficient but not necessary for class membership.
C) necessary and sufficient for class membership.
D) neither necessary nor sufficient for class membership.
Q2) A dataset of 1000 instances contains one attribute specifying the color of an object. Suppose that 800 of the instances contain the value red for the color attribute. The remaining 200 instances hold green as the value of the color attribute. What is the domain predictability score for color = green?
A) 0.80
B) 0.20
C) 0.60
D) 0.40
Q3) The single best representative of a class.
A) mean
B) centroid
C) signature
D) prototype
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Page 6
Chapter 5: Knowledge Discovery in Databases
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Sample Questions
Q1) The price of a 12 ounce box of cereal decreases from $3.50 to $3.00. What fraction is used to compute the percent decrease in the price of the cereal?
A) 1/3
B) 1/5
C) 1/6
D) 1/7
Q2) 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
Q3) This step of the KDD process model deals with noisy data.
A) Creating a target dataset
B) data preprocessing
C) data transformation
D) data mining
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Chapter 6: The Data Warehouse
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Sample Questions
Q1) The purpose of an intersection entity is to replace
A) two one-to-one relationships with a one-to-many relationship
B) two one-to-many relationships with one many-to-many relationship
C) a many-to-many relationship with two one-to-many relationships
D) a one-to-many relationship with two one-to-one relationships
Q2) Which of the following is an example of a dice operation?
A) Which region shows the smallest amount of total dollars spent on restaurant and travel for all quarters?
B) Select all cells where time=Q1 or Q2.
C) Provide a spreadsheet of category and region information for Q1.
D) Select all cells where category = travel, vehicle or retail.
Q3) This process removes redundancies that may be present in a data model.
A) abstraction
B) granularization
C) standardization
D) normalization
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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 hypothesis of no significant difference.
A) nil
B) invalid
C) null
D) void
Q3) The correlation between the number of years an employee has worked for a company and the salary of the employee is 0.75.
What can be said about employee salary and years worked?
A) There is no relationship between salary and years worked.
B) Individuals that have worked for the company the longest have higher salaries.
C) Individuals that have worked for the company the longest have lower salaries.
D) The majority of employees have been with the company a long time.
E) The majority of employees have been with the company a short period of time.
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Chapter 8: Neural Networks
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Sample Questions
Q1) Neural network training is accomplished by repeatedly passing the training data through the network while
A) individual network weights are modified.
B) training instance attribute values are modified.
C) the ordering of the training instances is modified.
D) individual network nodes have the coefficients on their corresponding functional parameters modified.
Q2) A two-layered neural network used for unsupervised clustering.
A) backpropagation network
B) Kohonen network
C) perceptron network
D) aggomerative network
Q3) 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.
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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) Regression trees are often used to model _______ data.
A) linear
B) nonlinear
C) categorical
D) symmetrical
Q2) 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.
Q3) This clustering algorithm merges and splits nodes to help modify nonoptimal partitions.
A) agglomerative clustering
B) expectation maximization
C) conceptual clustering
D) K-Means clustering
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Chapter 11: Specialized Techniques
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Sample Questions
Q1) A data mining algorithm designed to discover frequently accessed Web pages that occur in the same order.
A) serial miner
B) association rule miner
C) sequence miner
D) decision miner
Q2) A set of pageviews requested by a single user from a Web server.
A) index page
B) common log
C) session
D) page frame
Q3) 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
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Chapter 12: Rule-Based Systems
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Sample Questions
Q1) An internal test of an expert system whose purpose is to determine if the system uses the same reasoning process as the experts) used to build the system.
A) validation
B) verification
C) reliability
D) suitability
Q2) This reasoning strategy works best for problems where the goal can be stated as a question.
A) forward chaining
B) depth-first search
C) backward chaining
D) breadth-first search
Q3) A problem that cannot be solved with a computer using a traditional algorithmic technique.
A) exponentially hard problem
B) recursive problem
C) non-transformable problem
D) combinatorial problem
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Chapter 13: Managing Uncertainty in Rule-Based Systems
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Sample Questions
Q1) The probability that a person owns a sports car given that they subscribe to at least one automotive magazine is 40%. We also know that 3% of the adult population subscribes to at least one automotive magazine. Finally, the probability of a person owning a sports car given that they don't subscribe to at least one automotive magazine is 30%. Use this information together with Bayes theorem to compute the probability that a person subscribes to at least one automotive magazine given that they own a sports car.
Q2) Computing the probability of picking a heart from a deck of 52 cards can be determined using ______ probability technique.
A) an objective
B) an experimental
C) a subjective
D) an inexact
Q3) For Bayes theorem to be applied, the following relationship between hypothesis H and evidence E must hold.
A) PH|E) + PH| ~E) = 1
B) PH|E) + P~H| E) = 1
C) PH|E) + PH| ~E) = 0
D) PH|E) + P~H| E) = 0
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Page 15
Chapter 14: Intelligent Agents
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Sample Questions
Q1) An expert system contains _________ knowledge whereas the knowledge processed by an intelligent agent is _____________
A) personal, general
B) general, personal
C) direct, indirect
D) indirect, direct
Q2) An agent's ability to choose its actions in the context of other agents.
A) autonomy
B) cooperation
C) adaptivity
D) coordination
Q3) 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.
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