Data Mining and Data Warehousing Syllabus - BIM (TU)
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Course Description
Course Objective
The objective of the course is to make learner understand foundation principles and techniques
of data mining and data warehousing. Students will be able to select and use various data mining language and tools very useful for adding business value of an organization.
Course Description
Introduction, Data Preprocessing- Data Integration and Transformation, Classification, Association Analysis, Cluster Analysis, Information Privacy and Data Mining, Advanced Applications, Search engines, Data Warehouses, Capacity Planning.
Unit Contents
Course Details
Unit 1: Introduction LH 2
- Data Mining Origin
- Data Mining & Data Warehousing basics
Unit 2: Data Preprocessing LH 6
- Data Types and Attributes
- Data Pre-processing
- OLAP
- Characteristics of OLAP Systems
- Multidimensional View and Data cube
- Data Cube Implementation
- Data Cube Operations
- Guidelines for OLAP Implementation
Unit 3: Classification LH 7
- Basics and Algorithms
- Decision Tree Classifier
- Rule Based Classifier
- Nearest Neighbor Classifier
- Bayesian Classifier
- Artificial Neural Network Classifier
- Issues : Overfitting, Validation, Model Comparison
Unit 4: Association Analysis LH 7
- Basics and Algorithms
- Frequent Itemset Pattern & Apriori Principle
- FP-Growth, FP-Tree
- Handling Categorical Attributes
Unit 5: 5.1. |
Cluster Analysis Basics and Algorithms |
LH 7 |
5.2. |
K-means Clustering |
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5.3. |
Hierarchical Clustering |
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5.4. |
DBSCAN Clustering |
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Unit 6: 6.1 |
Information Privacy and Data Mining Basic principles to Protect Information Privacy |
LH 3 |
6.2 |
Uses and Misuses of Data Mining |
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6.3 |
Primary Aims of data Mining |
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6.4 |
Pitfalls of Data Mining |
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Unit 7: Advanced Applications LH 3
- Web-mining: Web content mining, web usage mining
- Time-series data mining
Unit 8: Search Engines LH 3
- Characteristics of search engine
- Search Engine functionality
- Ranking of Web pages
Unit 9: Data Warehousing LH 7
9.1 |
Operational Data sources |
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9.2 |
ETL (Extract, Transform, Load) |
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9.3 |
Data Warehouse Processes, Managers and their functions |
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9.4 |
Data Warehouses and Data Warehouses Design |
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9.5 |
Guidelines for Data Warehouse Implementation |
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Unit 10 10.1 |
Capacity Planning Calculating storage requirement, CPU requirements |
LH 3 |
Practical:
Students should practice enough on real-world data intensive problems
Text and Reference Books
References:
- Pang-NingTan, Michael Steinbach and Vipin Kumar, Introductionto Data Mining, 2005, Addison-
- Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 2nd Edition, 2006, Morgan
- K. Gupta, Introduction to Data Mining with Case Studies, Prentice Hall of India
- IBM, An Introduction to Building the Data Warehouse, Prentice Hall of India
- IBM, Introduction to Business Intelligence and Data Warehousing, Prentice Hall of India
- Adriaans Pieter, Zantige, "Data Mining", Pearson Education Asia Pub. Ltd, 2002
- Short Name N/A
- Course code IT 308
- Semester Eighth Semester
- Full Marks 100
- Pass Marks 45
- Credit 3 hrs
- Elective/Compulsary Elective