JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD MCA 2019 DATA WAREHOUSING AND DATAMINING Previous Question Paper (December - 2019 - R17)
This question paper contains two parts A and B. Part A is compulsory which carries 25 marks. Answer all questions in Part A. Part B consists of 5 Units. Answer any one full question from each unit. Each question carries 10 marks and may have a, b, c as sub questions.
Part A
Describe three challenges to data mining regarding data mining methodology and user interaction issues.
Answer
Three key challenges in data mining methodology and user interaction include:
- Mining different kinds of knowledge in databases: Data mining must cover a wide spectrum of tasks (classification, clustering, association, etc.) to satisfy diverse user requirements, requiring highly flexible and integrated systems.
- Handling noisy or incomplete data: Real-world data is often imperfect; algorithms must be robust enough to handle noise and missing values without compromising the accuracy of the discovered patterns.
- Interactive mining at multiple levels of abstraction: Users often need to drill down or roll up through data hierarchies. Providing an interactive, user-friendly interface that allows users to guide the mining process is essential for effective knowledge discovery.
Illustrate the OLAP operations in the Multidimensional Data Model.
Answer
OLAP operations allow users to analyze data from multiple perspectives in a multidimensional data cube:
- Roll-up: Performs aggregation on a data cube by climbing up a concept hierarchy or by dimension reduction.
- Drill-down: The reverse of roll-up; it navigates from less detailed data to more detailed data by introducing new dimensions or moving down a hierarchy.
- Slice and Dice: Slice performs a selection on one dimension of the cube resulting in a sub-cube. Dice defines a sub-cube by performing a selection on two or more dimensions.
- Pivot: A visualization operation that rotates the data axes in view to provide an alternative presentation of the multidimensional data.
How to develop a scalable SVM algorithm for efficient classification in large datasets?
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Discuss about Outlier detection using deviation based approach.
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Differentiate structured, unstructured and semi-structured data.
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Part B
What is Data Mining? Explain the steps involved in KDD with neat sketch.
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Describe the classification of Data Mining System.
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Distinguish the methods used for integration of Data Mining System with data warehousing system.
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Describe various methods involved in Data Cleaning process.
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Describe the similarities and the differences of star schema, and snowflake schema then analyze their advantages and disadvantages from each other.
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Discuss about the algorithm that computes closed iceberg cubes efficiently.
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A data cube C, has n dimensions, and each dimension has exactly p distinct values in the base cuboid. Assume that there are no concept hierarchies associated with the dimensions. (i) What is the maximum number of cells possible in the base cuboid? (ii) What is the minimum number of cells possible in the base cuboid? (iii) What is the maximum number of cells possible in the data cube, C? (iv) What is the minimum number of cells possible in the data cube, C?
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Discuss about bitmap indexing.
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A database has five transactions. Let min sup = 60% and min conf = 80%. Find all frequent item sets using Apriori and FP-growth, respectively. Compare the efficiency of the two mining processes.
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Discuss about Multilayer Feed-Forward Neural Network.
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Describe Back Propagation Algorithm for classification and explain with an example.
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Differentiate AGNES and DIANA clustering algorithms.
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Explain about k-means algorithm with an example.
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Given two objects represented by the tuples (22, 1, 42, 10) and (20, 0, 36, 8): a) Compute the Euclidean distance between the two objects. b) Compute the Manhattan distance between the two objects. c) Compute the Minkowski distance between the two objects, using q = 3.
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What is time-series data? Explain in brief about mining Time-series data.
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Enumerate the steps in Multimedia Data mining.
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Design a Web document classification method that can take index classification standard to classify a set of Web documents automatically.
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