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CY-604 (C) RGPV B.Tech

Dataware Housing & Mining (DHM)

Previous year papers and the official syllabus are free to read, no account needed.

Previous Year Papers

2 papers, latest Jun 2025

Free
See all 2 papers

Syllabus, notes and important questions for CY-604 (C)

Syllabus units

Topics as the official RGPV syllabus lists them.

Unit 1 Data Warehousing 9 topics
  • Need for data warehousing
  • Basic elements of data warehousing
  • Data Mart
  • Data Warehouse Architecture
  • Extract and load process
  • Clean and transform data
  • Star, Snowflake and Galaxy schemas for multidimensional databases
  • Fact and dimension data
  • Partitioning strategy – horizontal and vertical partitioning
Unit 1 notes
Unit 2 OLAP Technology and Data Warehouse Implementation 7 topics
  • Data Warehouse and OLAP technology
  • Multidimensional data models and different OLAP operations
  • OLAP server: ROLAP, MOLAP
  • Data Warehouse implementation
  • Efficient computation of data cubes
  • Processing of OLAP queries
  • Indexing data
Unit 2 notes
Unit 3 Data Mining 15 topics
  • Data preprocessing
  • Data integration and transformation
  • Data reduction
  • Discretization and concept hierarchy generation
  • Basics of data mining
  • Data mining techniques
  • KDP (Knowledge Discovery Process)
  • Application and challenges of data mining
  • Web structure mining
  • Web usage mining
  • Spatial mining
  • Text mining
  • Security issues
  • Privacy issues
  • Ethical issues
Unit 3 notes
Unit 4 Mining Association Rules in Large Databases 7 topics
  • Association rule mining
  • Single‑dimensional Boolean association rules
  • Multi‑level association rules
  • Apriori algorithm
  • Fp‑Growth algorithm
  • Time‑series mining association rules
  • Latest trends in association‑rules mining
Unit 4 notes
Unit 5 Classification and Clustering 12 topics
  • Distance measures for classification and clustering
  • Types of clustering
  • K‑Means algorithm
  • Decision tree induction
  • Bayesian classification
  • Association‑rule based classification
  • Other classification methods
  • Prediction
  • Classifier accuracy
  • Categorization of methods
  • Partitioning methods
  • Outlier analysis
Unit 5 notes
Read the full syllabus