CS703(B) Data Mining and Warehousing

RGPV Computer Science and Engineering VII Semester | Unit-wise Notes, Syllabus, Important Questions & PYQ Resources

📘 Subject Overview

Data Mining and Warehousing is an open elective subject in RGPV CSE 7th semester. This subject covers data warehousing, OLAP systems, data preprocessing, data mining, classification, clustering and association rule mining.

🎯 Course Objectives

Understand the value of historical data and data mining in solving real-world problems.
Become familiar with supervised and unsupervised learning algorithms used in data mining.
Develop skills to use data mining for solving real-world problems.

📚 Unit-wise Notes

Unit 1

Data Warehousing

Introduction, data warehouse architecture, preprocessing, cleaning, integration, reduction, data marts, metadata and multidimensional model.

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Unit 2

OLAP Systems

OLAP concepts, OLAP queries, types of OLAP servers, OLAP operations, hardware, security, backup and recovery.

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Unit 3

Data & Data Mining

Data types, data quality, preprocessing, similarity measures, summary statistics, KDD, issues in data mining and fuzzy logic.

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Unit 4

Supervised Learning

Classification, decision trees, neural networks, statistical algorithms, rule-based algorithms and probabilistic classifiers.

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Unit 5

Clustering & Association Rule Mining

Hierarchical clustering, partitional clustering, BIRCH, DBSCAN, CURE, Apriori and FP-Growth algorithms.

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📝 Complete Syllabus

Unit	Topics
Unit 1	Data Warehousing, architecture, preprocessing, cleaning, integration, transformation, reduction, schema, partitioning, data marts, metadata and multidimensional data model.
Unit 2	OLAP concepts, OLAP queries, OLAP servers, OLAP operations, operational design, security, backup and recovery.
Unit 3	Data types, quality of data, preprocessing, similarity measures, summary statistics, KDD, data mining issues, fuzzy sets and fuzzy logic.
Unit 4	Classification, statistical algorithms, distance-based algorithms, decision tree algorithms, neural networks, rule-based algorithms and probabilistic classifiers.
Unit 5	Hierarchical algorithms, partitional algorithms, clustering large databases, BIRCH, DBSCAN, CURE, Apriori and FP-Growth.

⭐ Most Important Exam Topics

Data Warehouse Architecture
Data Preprocessing
Data Cleaning, Integration and Reduction
Data Mart and Metadata
Multidimensional Data Model
OLAP Operations
OLAP Server Types
KDD Process
Classification Algorithms
Decision Tree Classifier
Clustering Algorithms
Apriori and FP-Growth Algorithm

📌 PYQ Analysis

PYQ Analysis content will be updated soon. For now, students should focus on Data Warehouse Architecture, OLAP operations, Data Preprocessing, KDD, Classification, Decision Tree, Clustering, Apriori and FP-Growth.

🎓 Branch / Semester

This page is useful for RGPV B.Tech Computer Science Engineering students, especially 7th semester students studying CS703(B) Data Mining and Warehousing.

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