CS703(B) Data Mining and Warehousing

📘 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, delivery process, data warehouse architecture, preprocessing, cleaning, integration, reduction, design, partitioning, data marts, metadata and multidimensional model.

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Unit 2

OLAP Systems

Basic OLAP concepts, OLAP queries, types of OLAP servers, OLAP operations, data warehouse hardware and operational design including security, backup and recovery.

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Unit 3

Data & Data Mining

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

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Unit 4

Supervised Learning

Classification, statistical-based algorithms, distance-based algorithms, decision tree algorithms, neural network algorithms, rule-based algorithms and probabilistic classifiers.

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Unit 5

Clustering & Association Rule Mining

Hierarchical algorithms, partitional algorithms, BIRCH, DBSCAN, CURE, Apriori and FP-Growth algorithms for association rule mining.

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📝 Complete Syllabus

Unit	Topics
Unit 1	Data Warehousing: introduction, delivery process, architecture, preprocessing, data cleaning, integration, transformation, reduction, design, schema, partitioning strategy, implementation, data marts, metadata, multidimensional data model and pattern warehousing.
Unit 2	OLAP Systems: basic concepts, OLAP queries, types of OLAP servers, OLAP operations, data warehouse hardware, operational design, security, backup and recovery.
Unit 3	Introduction to Data and Data Mining: data types, quality of data, preprocessing, similarity measures, summary statistics, data distributions, basic data mining tasks, data mining vs KDD, issues in data mining, fuzzy sets and fuzzy logic.
Unit 4	Supervised Learning: classification, statistical-based algorithms, distance-based algorithms, decision tree-based algorithms, neural network-based algorithms, rule-based algorithms and probabilistic classifiers.
Unit 5	Clustering and Association Rule Mining: hierarchical algorithms, partitional algorithms, clustering large databases, BIRCH, DBSCAN, CURE, Apriori and FP-Growth algorithms.

⭐ 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

For RGPV exams, focus on Data Warehouse Architecture, OLAP operations, Data Preprocessing, KDD, Classification, Decision Tree, Clustering, Apriori and FP-Growth. These topics are suitable for 7 marks and 14 marks answers.

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❓ FAQs

Is Data Mining and Warehousing scoring?

Yes, it is scoring because many questions are theory-based and diagram-based.

Which units are most important?

Unit 1, Unit 2, Unit 4 and Unit 5 are very important for exam preparation.

What should I study first?

Start with Data Warehouse basics, then OLAP, then Data Mining/KDD, then Classification and Clustering.

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