ANU UG/Degree 4th Sem(Y23) Data Warehousing and Data Mining Unit Wise Important Questions

 ANU UG/Degree 4th Sem(Y23) Data Warehousing and Data Mining Unit Wise Important Questions are now available, these questions are very important for your semester exams. These questions are prepared by top qualified faculty. Read these questions for good marks.

 

UNIT I

Data Warehousing and Business Analysis: - Data warehousing Components –Building a Data warehouse –Data Warehouse Architecture – DBMS Schemas for Decision Support – Data Extraction, Cleanup, and Transformation Tools –Metadata – reporting – Query tools and Applications – Online Analytical Processing (OLAP) – OLAP and Multidimensional Data Analysis.

Short Answer Questions

1. Define data warehousing and list its key components.
2. What are the essential steps involved in building a data warehouse?
3. Describe the typical architecture of a data warehouse.
4. What are DBMS schemas for decision support, and how do they differ from operational schemas?
5. Explain the role of data extraction, cleanup, and transformation tools in the data warehousing process.
6. What is Online Analytical Processing (OLAP) and how does it facilitate multidimensional data analysis?

Long Answer Questions

1. Discuss in detail the various components of a data warehouse, explaining how each contributes to effective business analysis.
2. Outline the process of building a data warehouse from planning through implementation, and analyze the challenges encountered during each phase.
3. Compare different data warehouse architectures (e.g., centralized, federated, data marts) and evaluate their advantages and disadvantages in a business context.
4. Explain the concept of DBMS schemas for decision support systems, detailing common schema models such as star schema and snowflake schema, and their impact on query performance.
5. Analyze the importance of data extraction, cleanup, and transformation (ETL) tools in ensuring data quality within a data warehouse, and discuss best practices for ETL processes.
6. Elaborate on the functionalities of Online Analytical Processing (OLAP), describing its role in multidimensional data analysis and reporting, and provide examples of how OLAP tools support business decision-making.

UNIT II

Data Mining: - Data Mining Functionalities – Data Preprocessing – Data Cleaning – Data Integration and Transformation – Data Reduction – Data Discretization and Concept Hierarchy Generation- Architecture Of A Typical Data Mining Systems- Classification Of Data Mining Systems.

Association Rule Mining: - Efficient and Scalable Frequent Item set Mining Methods – Mining Various Kinds of Association Rules – Association Mining to Correlation Analysis – Constraint-Based Association Mining.

Short Answer Questions

1. What are the key functionalities of data mining?
2. What is data preprocessing, and how do data cleaning, integration, and transformation contribute to this process?
3. How does data reduction enhance the efficiency of data mining operations?
4. Explain data discretization and the process of concept hierarchy generation in data mining.
5. Describe the architecture of a typical data mining system and identify its main components.
6. How are data mining systems classified based on their functionalities and architecture?

Long Answer Questions

1. Discuss in detail the key functionalities of data mining and provide examples of how these functionalities are applied in real-world scenarios.
2. Explain the data preprocessing phase in data mining by covering data cleaning, data integration, and data transformation. What challenges and best practices are involved?
3. Analyze the role of data reduction in data mining. Describe various techniques used and their impact on processing efficiency.
4. Elaborate on the methods of data discretization and concept hierarchy generation, discussing their benefits and potential limitations.
5. Describe the architecture of a typical data mining system, including a detailed explanation of its main components and their interactions.
6. Compare and contrast different classifications of data mining systems, discussing how data types, mining techniques, and application domains influence their categorization.

UNIT III
Classification and Prediction: - Issues Regarding Classification and Prediction – Classification by Decision Tree Introduction – Bayesian Classification – Rule Based Classification – Classification by Backpropagation – Support Vector Machines – Associative Classification – Lazy Learners – Other Classification Methods – Prediction – Accuracy and Error Measures – Evaluating the Accuracy of a Classifier or Predictor – Ensemble Methods – Model Section. 

Short Answer Questions

1. What are the primary challenges and issues associated with classification and prediction in data mining?
2. Explain the basic working principle of decision tree classification.
3. How does Bayesian classification operate, and what key assumption underpins its methodology?
4. Define rule-based classification and describe one real-world application.
5. What are Support Vector Machines (SVMs), and why are they effective in handling complex classification tasks?
6. Briefly explain the concept of ensemble methods and their role in improving the accuracy of classifiers.

Long Answer Questions

1. Discuss the various issues related to classification and prediction, such as overfitting, underfitting, and high-dimensionality. Provide strategies to address these challenges.
2. Compare and contrast different classification techniques—including decision trees, Bayesian methods, rule-based systems, and backpropagation-based classification—highlighting their strengths, weaknesses, and suitable application scenarios.
3. Elaborate on Support Vector Machines (SVMs) and associative classification methods. Describe how these techniques work and discuss the advantages and limitations of each.
4. Define lazy learners in the context of classification. Explain how they differ from eager learners and provide examples of when lazy learning might be preferred.
5. Explain the concept of prediction in data mining, detailing various accuracy and error measures used to evaluate classifiers or predictors. Discuss how these metrics influence model selection.
6. Analyze ensemble methods and model selection in classification and prediction. Describe common ensemble techniques, such as bagging and boosting, and explain how they contribute to improved predictive performance.

 UNIT IV
Cluster Analysis: - Types of Data in Cluster Analysis – A Categorization of Major Clustering Methods – Partitioning Methods – Hierarchical methods – Density-Based Methods – Grid-Based Methods – Model-Based Clustering Methods – Clustering High-Dimensional Data – Constraint-Based Cluster Analysis – Outlier Analysis.

Short Answer Questions

1. What types of data are typically used in cluster analysis, and how do they influence the clustering process?
2. Define partitioning methods in cluster analysis and provide one example.
3. How does hierarchical clustering differ from partitioning methods?
4. What is the main principle behind density-based clustering methods?
5. Describe grid-based clustering and mention one key advantage it offers when handling large datasets.
6. What is outlier analysis in cluster analysis, and why is it important?

Long Answer Questions

1. Discuss the various types of data used in cluster analysis and explain how the characteristics of these data types can affect the choice of clustering method.
2. Provide a detailed comparison of the major clustering methods—partitioning, hierarchical, density-based, grid-based, and model-based. Include their key features, advantages, and limitations.
3. Explain partitioning methods in depth. Describe common algorithms, how they work, and discuss scenarios where partitioning methods are most effective.
4. Compare and contrast hierarchical clustering with density-based clustering. Highlight the underlying mechanisms, the types of clusters each method tends to identify, and their applicability in different contexts.
5. Describe grid-based and model-based clustering methods. Discuss their methodologies and evaluate their effectiveness in processing high-dimensional and large-scale datasets.
6. Elaborate on constraint-based cluster analysis and the role of outlier analysis. How do constraints improve clustering outcomes, and what techniques are used to identify and manage outliers in clusters?

UNIT V

Mining Object, Spatial, Multimedia, Text and Web Data: Multidimensional Analysis and Descriptive Mining of Complex Data Objects – Spatial Data Mining – Multimedia Data Mining – Text Mining – Mining the World Wide Web.

Short Answer Questions

1. What is meant by mining complex data objects and how does multidimensional analysis support descriptive mining?
2. How does spatial data mining differ from traditional data mining methods?
3. What are some key challenges unique to multimedia data mining?
4. Define text mining and mention one common technique used in extracting useful information from textual data.
5. What distinguishes web mining from other forms of data mining?
6. Explain the concept of descriptive mining and its significance in analyzing complex data objects.

Long Answer Questions

1. Discuss in detail the process of multidimensional analysis and descriptive mining of complex data objects. How do these techniques differ from standard data mining approaches, and what advantages do they offer?
2. Explain the methodologies and challenges associated with spatial data mining. Provide examples of spatial data and discuss its practical applications in various industries.
3. Elaborate on multimedia data mining by describing the techniques used to process and analyze multimedia content, as well as the challenges involved in handling diverse data types such as images, audio, and video.
4. Provide a comprehensive overview of text mining, including key preprocessing steps, common algorithms, and real-world applications in areas such as sentiment analysis and information retrieval.
5. Compare and contrast web mining with traditional data mining techniques. What unique challenges does mining the World Wide Web present, and how are these challenges typically addressed?
6. Evaluate how integrating mining of object, spatial, multimedia, text, and web data can enhance modern business analytics. Discuss how these diverse mining techniques complement each other to provide a more comprehensive view of data.