Mining of Massive Datasets
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Mining of Massive Datasets Syllabus — Unit-wise Content
The official GTU curriculum for Mining of Massive Datasets (subject code 3174302) as prescribed for AI & DS · Semester 7 divides the course into 7 units, covering 36 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 (3174302)Unit 1 Data Mining
- Concepts and Techniques
- Data Mining Process
- Statistical Modeling
- Computational Approaches to Modeling with Machine Learning
- Feature Extraction
- Bonferroni’s Principle
Unit 2 Distributed File System and MapReduce
- Physical Organization of Compute Nodes
- Large Scale File System Organization
- Map Task
- Grouping by Key, The Reduce Task
- MapReduce Execution
- Algorithms using MapReduce – Matrix- Vector Multiplication
- Computation Selection and Projection
- Grouping and Aggregation
- Communication Cost Model
- Mapping Schemas
Unit 3 Similarity Estimation, Min Hashing and Jaccard Similarity,
- Locality Sensitive Hashing
- Distance Measures
- Membership Test
- Bloom Filters
- Large Scale Optimization Subject Code: 3174302 Subject Name: Mining of Massive Datasets
Unit 4 Mining Data Streams, Sampling, Filtering, Counting Distance,
- Estimating Moments
- Link Analysis
- Page Rank
- Link Spam
- Hubs and Authorities
Unit 5 Frequent Item sets, Market Baskets and the A-Priori Algorithm,
- Handling Larger Datasets in Main Memory
- Limited-Pass Algorithms
- Counting Frequent Items in a Stream
Unit 6 Clustering, Introduction to Clustering Techniques, Hierarchical
- K-means Algorithms
- CURE Algorithm
- Clustering in Non-Euclidean Spaces
- Clustering for Streams and Parallelism
Unit 7 Analysis of Large Graphs, Mining Social-Network Graphs,
- Recommendation Systems
- Dimensionality Reduction
- Large- Scale Machine Learning
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