JNTU
JNTU MCA 2022 DATA WAREHOUSING AND DATAMINING Previous Question Paper (March/April - 2022 - R17)
Answer any five questions
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Main
Discuss the major issues in data mining.
Answer
Data mining faces several significant challenges that impact its effectiveness and applicability:
- Methodology and User Interaction: Mining different kinds of knowledge requires diverse techniques. Users often need to interact with the system to guide the discovery process, requiring expressive query languages and visualization tools.
- Performance and Scalability: Algorithms must be highly efficient to handle massive datasets. Techniques like parallel processing, incremental updating, and sampling are essential to ensure acceptable response times.
- Data Diversity: Mining complex data types, such as multimedia, spatial, temporal, and web data, requires specialized algorithms that go beyond traditional relational database techniques.
- Evaluation and Quality: Discovered patterns must be evaluated for validity, novelty, and utility. Handling noisy, incomplete, or inconsistent data is a critical requirement for producing reliable results.
- Privacy and Security: Protecting sensitive information and ensuring data anonymity is paramount, especially when mining personal or proprietary data.
Differentiate between data warehouse and data mining.
Answer
| Feature | Data Warehouse | Data Mining |
|---|---|---|
| Definition | A subject-oriented, integrated, time-variant, and non-volatile collection of data. | The process of discovering interesting patterns and knowledge from large amounts of data. |
| Purpose | Provides a platform for data storage, reporting, and online analytical processing (OLAP). | Extracts hidden patterns, trends, and predictive models from stored data. |
| Operation | Focuses on data integration, consolidation, and historical storage. | Focuses on pattern recognition, classification, clustering, and association. |
| Dependency | Acts as the source or foundation for data mining activities. | Relies on the data warehouse to provide clean, integrated, and historical data. |
| Output | Structured reports, dashboards, and multidimensional data cubes. | Models, rules, clusters, and predictive insights. |
Give a brief summary on classification of data mining systems.
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Write short notes on data mining task primitives.
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What is data warehouse? Discuss the characteristics of data warehouse.
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Describe the measures of multidimensional data model.
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Explain in detail types of OLAP servers and various OLAP operations with examples.
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Differentiate between Eager learners and Lazy learners.
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Explain classification by back propagation.
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Explain about Bayesian classification. Illustrate with an example.
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Give a brief summary on support vector machines.
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What is cluster analysis? Explain the various types of data in cluster analysis.
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Give a brief summary on categorization of major clustering methods.
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Explain the following:
Mining time-series data.
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Spatial data mining
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