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WorksheetsBSCS 4-2 - Elective 4 - Midterm Examination - 2023
Total questions: 35
Worksheet time: 18mins
Which data mining task aims to assign predefined labels to instances based on labeled training data?
Clustering
Association Rule Mining
Classification
Regression
Which of the following best describes the process of Extract, Transform, Load (ETL) in data warehousing?
Extracting data from a data warehouse for reporting purposes
Loading data from external sources into a data warehouse
Transforming raw data into a structured format for analysis
Extracting data from transactional databases for storage in a data warehouse
Which data cube concept allows users to view data at different levels of detail or summarization?
Roll-up
Slicing
Dicing
Drill-down
What is a data cube in the context of data warehousing?
A storage container for unstructured data
A visualization tool for analyzing data
A database schema for transactional data
A multi-dimensional representation of data for
What is the primary benefit of using a data cube in data warehousing?
Improved data security
Reduced data storage requirements
Enhanced data quality
Faster Data Retrieval
Which of the following is NOT a characteristic of a data cube?
Dimensions
Rows and columns
Hierarchies
Measures
Which operation allows users to select a subset of data from a data cube by fixing the values of one or more dimensions?
Roll-up
Slicing
Dicing
Drill-down
What is the role of numerical attributes in data mining?
To represent qualitative information
To classify data objects into categories
To provide quantitative measurements or values
To define relationships between data objects
One of the activities performed in data mining is summarization, which means________?
To find out the group of objects which are like each other but are different from the object in another group
To find out the overall generated data and its calculation in an easily comprehensible and informative manner
To find out the continuous quantity for new observations using the knowledge gained from the previous data
To accurately find out the target class for each case in the data
Which of the following statement describes the difference between discrete and continuous attributes?
Discrete attributes can take on any value within a range, while continuous attributes have a finite set of distinct values
Discrete attributes represent qualitative variables, while continuous attributes represent quantitative variables
Discrete attributes are binary variables, while continuous attributes are multivalued variables
Discrete attributes have distinct values or categories, while continuous attributes can take on any value within a range
What are the sub-stages included in data preparation?
Cleaning, Integration, Selection, Transformation
Cleaning, Integration, Selection, Data Mining
Cleaning, Selection, Integration, Evaluation
Cleaning, Transformation, Evaluation, Data Mining
Aside from covering an advance tools and technologies to gain data, data mining aims to maximize those limited resources and materials such as the space and time complexity, which falls to:
Identification
Prediction
Optimization
Classification
It is a type of dataset that includes data on customers payments, banking, shopping, etc.
Customer behavior dataset
Transactional dataset
Market basket dataset
Streaming dataset
Data mining issue that deals with uncertain data which sometimes lead to wrong or incorrect pattern.
Handling Noisy or Incomplete Data
Pattern Evaluation
Mining Information from Heterogeneous Databases and Global Information
Handling Relational and Complex Types of Data
Which type of dataset represents data that has a geographical component?
Static dataset
Time series dataset
Transactional dataset
Spatial dataset
Which type of data is suitable for representing hierarchical relationships?
Record data
Graph data
Ordered data
Categorical data
Which type of data is commonly used to represent social networks or web page linkages?
Record data
Graph data
Ordered data
Categorical data
Which type of data is suitable for representing non-sequential or non-hierarchical relationships?
Record data
Graph data
Ordered data
Categorical data
A dataset that includes data on time-based measurements or observations taken at regular intervals
Static dataset
Time series dataset
Transactional dataset
Spatial dataset
Which of the following is NOT a major task in data preprocessing?
Data integration
Data cleaning
Data Visualization
Data Normalization
Which task in data preprocessing involves encoding categorical variables into numerical representations?
Data cleaning
Data transformation
Data normalization
Data integration
You have collected data from different sources with varying formats and structures. What task in data preprocessing would you perform to combine this data into a single dataset for analysis?
Data cleaning
Data transformation
Data normalization
Data integration
You have a dataset where the values of different variables have different scales. Which task in data preprocessing would you perform to bring all variables to a common scale?
Data cleaning
Data transformation
Data normalization
Data integration
You are analyzing a transaction dataset of a grocery store. You want to identify associations between items frequently purchased together. Which technique would you use for this analysis?
Association rule mining
Cluster analysis
Decision tree mining
Time series analysis
You have discovered an association rule with a high support value but a low confidence value. What does this indicate?
The rule is irrelevant
The rule is statistically significant
The rule is reliable
The rule is noisy
You are performing association rule mining on a dataset and want to focus on discovering only strong associations. Which parameter would you adjust to achieve this?
Minimum support threshold
Minimum confidence threshold
Maximum lift threshold
Maximum number of iterations
In association rule mining, the process of generating rules with larger itemsets by combining smaller frequent itemsets is known as:
Reduction
Pruning
Extraction
Joining
What is the purpose of pruning in association rule mining?
To remove irrelevant attributes from the dataset
To reduce the number of association rules generated
To transform categorical variables into numerical representations
To eliminate infrequent itemsets during the mining process
You want to predict the price of a house based on its features like size, number of bedrooms, and location. Which algorithm would be suitable for this regression task?
K Nearest Neighbor
Logistic regression
K-means clustering
Multiple linear regression
You have a dataset with a mix of numerical and textual features, and you want to perform classification. Which algorithm can handle this type of data?
Logistic regression
K Nearest Neighbor
Decision tree
Random Forest
You have a dataset of emails labeled as spam and non-spam, and you want to classify new incoming emails. Which algorithm would be suitable for this task?
Decision tree
Support Vector Machines (SVM)
Naive Bayes
K nearest neighbors
In Naive Bayes, how are the likelihoods of the features computed?
By assuming a Gaussian distribution for the features
By estimating the probability distributions of the features
By summing the feature values for each class
By applying a logarithmic transformation to the feature values
The Naive Bayes algorithm involves calculating the posterior probability of a class given the feature values. How is this probability computed?
By applying Bayes' theorem
By computing the prior probability of the class
By multiplying the likelihoods of the features
By normalizing the probabilities
You are working on a classification problem with a dataset containing a large number of features. Which algorithm would be suitable for handling high-dimensional data and avoiding overfitting?
Logistic Regression
Naive Bayes
K-nearest neighbors
Random Forest
Random Forest is an example of which type of machine learning algorithm?
Unsupervised learning
Supervised learning
Reinforcement learning
Semi-supervised learning
