JNTU
JNTU MCA 2018 DATA WAREHOUSING AND DATA MINING Previous Question Paper (January - 2018 - R13)
Part A is compulsory. Part B consists of 5 Units. Answer any one full question from each unit.
Part A
Discuss the data smoothing techniques.
Answer
Data smoothing techniques are used to remove noise from data and improve data quality. Key methods include:
- Binning: Data is sorted and partitioned into equal-width or equal-depth bins. Smoothing is performed by replacing values with the bin mean, median, or boundaries.
- Regression: Data is smoothed by fitting the data to a function, such as linear or multiple regression, to estimate values and minimize errors.
- Clustering: Outliers are detected and removed by grouping similar data points; values outside of clusters are treated as noise.
Discuss the OLAP query processing.
Answer
OLAP query processing involves efficient retrieval of multidimensional data from a data cube. It utilizes specific operations to navigate data:
- Roll-up: Aggregates data by climbing up a concept hierarchy or reducing dimensions.
- Drill-down: Navigates from less detailed data to more detailed data by introducing new dimensions or descending hierarchies.
- Slice and Dice: Performs a selection on one dimension (slice) or a sub-cube selection (dice).
- Pivot: Rotates the data axes to provide an alternative view of the data.
Processing is optimized using techniques like materialized views, indexing (bitmap or join indexes), and pre-computation of aggregates.
Discuss constraint-based Association mining.
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How does tree pruning work?
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Why wavelet transformation useful for clustering?
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Part B
Write the syntax for the following data mining primitives:
The kind of knowledge to be mined.
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Measures of pattern interestingness.
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Briefly discuss the data mining functionalities.
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Briefly discuss the major issues in data mining regarding performance and diverse database types.
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Justify the role of data cube aggregation in data reduction process with an example.
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Differentiate operational database systems and data warehousing.
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What is data warehousing? Give their applications.
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Briefly discuss data warehouse architecture.
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Compare and contrast Apriori algorithm with frequent pattern growth algorithm. Consider a data set apply both algorithms and explain the results.
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Explain how concept hierarchies are used in mining multilevel association rule?
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Give the classification of association rules in detail.
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Discuss the five criteria for the evaluation of classification and prediction methods.
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Explain how rules can be extracted from training neural networks.
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Explain the hold out method for estimating classifier accuracy.
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Discuss Fuzzy set approach for classification.
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Write k-Means and k-Medoids algorithms.
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Explain COBWEB model.
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Explain about Statistical-based outlier detection and Deviation-based outlier detection.
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Given two objects represented by the tuples (22,1,42,10) and (20,0,36,8). i) Compute the Manhatten distance between the two objects. ii) Compute the Euclidean distance between the two objects.
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