Departmental Elective • IT603(B)

Data Mining RGPV IT 6th Semester

Complete subject page based on the RGPV New Scheme under AICTE Flexible Curricula. Study data warehousing, preprocessing, association rule mining, classification, clustering, web mining, spatial mining, temporal mining, text mining and related issues.

Explore Units Course Objectives
Home › Information Technology › 6th Semester › Data Mining

Course Objectives

The course is designed to build understanding of data warehouse systems, knowledge discovery, association-rule mining, classification and clustering methods.

Objective 1 To introduce data warehouse and its components.
Objective 2 To introduce the knowledge discovery process, data mining and its functionalities.
Objective 3 To develop understanding of various algorithms for association rule mining and their differences.
Objective 4 To introduce various classification techniques.
Objective 5 To introduce various clustering algorithms.

Unit-Wise Data Mining Syllabus

Open any unit to study the complete topic in RGPV exam-oriented format.

01

Unit I – Data Warehousing

  • 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
  • Fact and Dimension Data
  • Horizontal and Vertical Partitioning
  • Data Warehouse and OLAP Technology
  • Multidimensional Data Models
  • OLAP Operations
  • ROLAP and MOLAP
  • Data Warehouse Implementation
  • Efficient Computation of Data Cubes
  • Processing of OLAP Queries
  • Indexing Data
Open Unit 1 Notes
02

Unit II – Data Mining Fundamentals

  • Data Preprocessing
  • Data Integration and Transformation
  • Data Reduction
  • Discretization
  • Concept Hierarchy Generation
  • Basics of Data Mining
  • Data Mining Techniques
  • KDP – Knowledge Discovery Process
  • Applications of Data Mining
  • Challenges of Data Mining
Open Unit 2 Notes
03

Unit III – Association Rule Mining

  • 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 Rule Mining
Open Unit 3 Notes
04

Unit IV – Classification and Clustering

  • Distance Measures
  • Types of Clustering Algorithms
  • K-Means Algorithm
  • Decision Tree
  • Bayesian Classification
  • Other Classification Methods
  • Prediction
  • Classifier Accuracy
  • Categorization of Methods
  • Outlier Analysis
Open Unit 4 Notes
05

Unit V – Advanced Mining Techniques

  • Introduction to Web Mining
  • Types of Web Mining
  • Spatial Mining
  • Temporal Mining
  • Text Mining
  • Security Issues
  • Privacy Issues
  • Ethical Issues
Open Unit 5 Notes
Exam Focus: Prepare definitions, diagrams, architectures, algorithms, comparison tables, step-by-step working and advantages or limitations for each major topic.

Important Subject Areas

Data Warehousing Architecture, schemas and OLAP Data Preprocessing Cleaning, reduction and transformation Association Rules Apriori and FP-Growth Classification Decision Tree and Bayesian methods Clustering Distance measures and K-Means Web and Text Mining Advanced mining and ethical issues

Download Study Resources

Data Mining study material will be added here as it becomes available.

📘

Complete Notes

Complete IT603(B) Data Mining notes.

Coming Soon
⭐

Important Questions

Most expected RGPV examination questions.

Coming Soon
📄

PYQ Analysis

Previous-year question analysis and repeated topics.

Coming Soon

Frequently Asked Questions

The subject code is IT603(B) for Information Technology 6th Semester.
The syllabus contains five units covering data warehousing, preprocessing, association rules, classification, clustering and advanced mining techniques.
Apriori, FP-Growth, K-Means, Decision Tree and Bayesian Classification are especially important.
Unit I covers data warehousing, schemas, fact and dimension data, OLAP, ROLAP, MOLAP, data cubes, query processing and indexing.
Yes. Unit V includes security, privacy and ethical issues related to mining.

Related Pages

Home RGPV Notes Hub IT Branch Information Technology notes IT 6th Semester Explore all semester subjects