BSc CSIT · Semester VII
Data Warehousing and Data Mining syllabus
Official TU syllabus for Data Warehousing and Data Mining (CSC420): 9 units, 78 topics, 3 credit hours. Every unit links to its notes and solved questions.
1
Introduction to Data Warehousing
5h · 15 Q- Lifecycle of data
- Types of data
- Data warehouse and data warehousing
- Differences between operational database and data warehouse
- A multidimensional data model
- OLAP operation in multidimensional data model
- Conceptual modeling of data warehouse
- Architecture of data warehouse
- Data warehouse implementation
- Data marts
- Components of data warehouse
- Need for data warehousing
- Trends in data warehousing
2
Introduction to Data Mining
2h · 7 Q- Motivation for data mining
- Introduction to data mining system
- Data mining functionalities
- KDD
- Data object and attribute types
- Statistical description of data
- Issues and Applications
3
Data Preprocessing
3h · 5 Q- Data cleaning
- Data integration and transformation
- Data reduction
- Data discretization and Concept Hierarchy Generation
- Data mining primitives
4
Data Cube Technology
4h · 7 Q- Efficient method for data cube computation
- Cube materialization (Introduction to Full cube, Iceberg cube, Closed cube, Shell cube)
- General strategies for cube computation
- Attribute oriented induction for data characterization
- Mining class comparison
- Discriminating between different classes
5
Mining Frequent Patterns
6h · 6 Q- Frequent patterns
- Market basket analysis
- Frequent itemsets, closed itemsets, association rules
- Types of association rule (Single dimensional, multidimensional, multilevel, quantitative)
- Finding frequent itemset (Apriori algorithm, FP growth)
- Generating association rules from frequent itemset
- Limitation and improving Apriori
- From Association Mining to Correlation Analysis
- Lift
6
Classification and Prediction
10h · 11 Q- Definition (Classification, Prediction)
- Learning and testing of classification
- Classification by decision tree induction
- ID3 as attribute selection algorithm
- Bayesian classification
- Laplace smoothing
- Classification by backpropagation
- Rule based classifier (Decision tree to rules, rule coverage and accuracy, efficient of rule simplification)
- Support vector machine
- Evaluating accuracy (precision, recall, f-measure)
- Issues in classification
- Overfitting and underfitting
- K-fold cross validation
- Comparing two classifier (McNemar's test)
7
Cluster Analysis
8h · 9 Q- Types of data in cluster analysis
- Similarity and dissimilarity between objects
- Clustering techniques: Partitioning (k-means, k-means++, Mini-Batch k-means, k-medoids)
- Hierarchical (Agglomerative and Divisive)
- Density based (DBSCAN)
- Outlier analysis
8
Graph Mining and Social Network Analysis
5h · 5 Q- Graph mining
- Why graph mining
- Graph mining algorithm (Beam search, Inductive logic programming)
- Social network analysis
- Link mining
- Friends of friends
- Degree assortativity
- Signed network (Theory of structured balance, Theory of status, Conflict between the theory of balance and status)
- Trust in a network (Atomic propagation, Propagation of distrust, Iterative propagation)
- Predicting positive and negative links
9
Mining Spatial, Multimedia, Text and Web Data
2h · 5 Q- Spatial data mining
- Spatial data cube
- Mining spatial association
- Multimedia data mining
- Similarity search in multimedia data
- Mining association in multimedia data
- An introduction to text mining, natural language processing and information extraction
- Web mining (Web content mining, Web structure mining, Web usage mining)
Textbooks and references
- Data Mining: Concepts and Techniques, 3rd ed. Jiawei Han, Micheline Kamber, and Jian Pei. Morgan Kaufmann Series in Data Management Systems Morgan Kaufmann Publishers, July 2011.
- Introduction to Data Mining, 2nd ed. Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar. Pearson Publisher, 2019.
- Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman, 2014.
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