BIT · Semester VIII
Data Warehousing and Data Mining syllabus
Official TU syllabus for Data Warehousing and Data Mining (BIT454): 8 units, 51 topics. Every unit links to its notes and solved questions.
1
Data Warehouse Fundamentals
5 Q- Data warehouse definition and characteristics
- Operational database vs data warehouse
- Data warehouse components and architecture
- Data warehouse conceptual modeling techniques
- OLAP technology and multidimensional analysis
- Full cube and closed cube concepts
- Beam search mechanism
2
Data Preprocessing and Preparation
1 Q- Need for data preprocessing
- Data cleaning and transformation methods
- Data integration and consolidation
- Data reduction techniques
- Handling missing values and noise
3
Data Mining Fundamentals and Goals
2 Q- Data mining definition and scope
- Data mining functionalities and tasks
- Data mining goals and objectives
- Classification and prediction
- Clustering and segmentation
- Association rule mining
- Outlier detection and analysis
4
Classification Techniques
3 Q- Classification by back propagation
- Bayesian classification methods
- Decision tree induction
- Gini index for attribute selection
- Support vector machines
- Lazy learners and instance-based learning
- Ensemble methods
5
Clustering Methods
5 Q- Clustering approaches and types
- K-means algorithm and limitations
- Agglomerative clustering approach
- Divisive clustering approach
- Types of data in clustering
- High dimensional data clustering
- Similarity measures for ordinal data
6
Association Rule Mining
2 Q- Market basket analysis concept
- Frequent itemset generation
- Apriori algorithm
- Strong association rules
- Support and confidence measures
- Laplace smoothing
7
Advanced Mining Techniques
3 Q- Text mining definition and applications
- Web structure mining
- Web content mining
- Web usage mining
- Outlier analysis and detection
- Outlier definition and types
8
Social Network Analysis
3 Q- Social network analysis methods
- Social network analysis applications
- Types of network analysis
- Viral marketing concept
- Densification power law
- Network structure and properties
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