Data Warehousing and Data Mining
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Data Warehousing and Data Mining Syllabus — Unit-wise Content
The official GTU curriculum for Data Warehousing and Data Mining (subject code 3164305) as prescribed for AI & DS · Semester 6 divides the course into 5 units, covering 52 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3164305)Unit 1 Data Warehousing
- OLAP & OLTP
- Data warehouse & Data mart
- OLAM architecture
- Transform & Loading (ETL) concept for generic
- three - tier architecture
- Data warehousing schema - Star
- Fact Constellation (Galaxy) - Data Cube
- Operations on Data cube (slicing
- roll up, roll down
- drill up etc) 5 10 1 Introduction to data mining (DM): Motivation for Data Mining - Data Mining-Definition and Functionalities – Classification of DM Systems - DM task primitives - Integration of a Data Mining system with a Database or a Data Warehouse - Issues in DM – KDD Process
Unit 2 Data Pre-processing
- Data summarization
- data cleaning
- data integration and transformation
- data reduction
- data discretization and concept hierarchy generation
- feature extraction
- feature transformation
- feature selection
- introduction to Dimensionality Reduction
- CUR decomposition
Unit 3 Mining Frequent Patterns, Associations and Correlations
- Efficient and scalable frequent item-set mining methods
- mining various kind of association rules
- from association mining to correlation analysis
- Advanced Association Rule Techniques
- Measuring the Quality of Rules
Unit 4 Classification and Prediction
- Classification vs. prediction
- Issues regarding classification and prediction
- Statistical-Based Algorithms
- Distance-Based Algorithms
- Decision Tree- Based Algorithms
- Neural Network-Based Algorithms
- Rule-Based Algorithms
- Combining Techniques
- accuracy and error measures
- evaluation of the accuracy of a classifier or predictor
- Neural Network Prediction methods: Linear and nonlinear regression
- Logistic Regression Introduction of tools such as DB Miner / WEKA / DTREG DM Tools
Unit 5 Cluster Analysis
- 10 20 Subject Code: 3164305 Clustering: Problem Definition
- Clustering Overview
- Evaluation of Clustering Algorithms
- Partitioning Clustering -K-Means Algorithm
- K- Means Additional issues
- PAM Algorithm
- Hierarchical Clustering – Agglomerative Methods and divisive methods
- Basic Agglomerative Hierarchical Clustering
- Strengths and Weakness
- Outlier Detection
- Clustering high dimensional data
- clustering Graph and Network data. 8 Advance topics: Introduction to Web Mining
- Spatial Data Mining
- Temporal Mining
- Text Mining and Multimedia Mining
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