21DS51 Dayananda Sagar University • CSE — Data Science (Semester 5)

Data Mining and Warehousing

💡 DSU Exam Strategy & Guidance

Data Preprocessing, Star and Snowflake Schemas, Apriori Algorithm (Frequent Itemset Mining), FP-Growth, and DBSCAN Clustering.

High-Yield Passing Strategy: Apriori candidate generation step-by-step is asked in almost every question paper.
Most Repeated Question Topics:
Apriori Association Rule GenerationStar Schema vs Snowflake Schema DesignOLAP Operations (Roll-up, Drill-down, Slice, Dice)DBSCAN vs K-Means Comparison

🔥 Most Predictable Exam Questions (1)

Guaranteed Every Year 8 Marks Module 2: Association Rule Mining

Q1. Generate all frequent itemsets and strong association rules for a given transaction database with minimum support = 50% and minimum confidence = 75% using the Apriori Algorithm.

Key Points: Generate candidate 1-itemsets C1, prune below min_sup => L1. Join L1 to create C2, prune => L2. Continue to L3. For frequent itemset {I1, I2, I3}, generate rules and calculate Confidence(A -> B) = Support(A U B) / Support(A).

🎓 Open Source Courses & Video Playlists (2)

Stanford Online

Stanford CS246: Mining Massive Datasets

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IIT Kharagpur / NPTEL

NPTEL: Data Mining

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📄 Previous Year Question Papers (1)

END_SEM • 2024

End Semester Exam 2024

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