Data Warehousing and Data Mining
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Follow on Instagram for UpdatesData Warehousing and Data Mining Syllabus — Unit-wise Content
The official GTU curriculum for Data Warehousing and Data Mining (subject code ME01095031) as prescribed for AIAD · Semester 1 divides the course into 5 units, covering 38 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 (ME01095031)Unit 1 Basics of data warehousing and data mining
- Data Warehousing and Business Analysis: - Data warehousing Components –Building a Data warehouse –Data Warehouse Architecture – DBMS Schemas for Decision Support – Data Extraction
- Clean up, and Transformation Tools –Metadata – reporting – Query tools and Applications – Online Analytical Processing (OLAP) – OLAP and Multidimensional Data Analysis
- 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 Program Name: Master of Engineering Level: PG Branch: Computer Engineering (Artificial Intelligence And Data Science) Course / Subject Code : ME01095031 Subject Name: Data Warehousing and Data Mining
Unit 2 Basics of classification and prediction and clustering
- Classification and Prediction: - Classification by Decision Tree
- classification by back propagation
- Associative Classification – Lazy Learners Prediction – Accuracy and Error Measures – Evaluating the Accuracy of a Classifier or Predictor – Ensemble Methods – Model Section
- Classification vs. prediction Types of Data in Cluster Analysis – A Categorization of Major Clustering Methods – Clustering High-Dimensional Data – Constraint-Based Cluster Analysis – Outlier Analysis
Unit 3 Mining Time series Data & Data Streams
- Periodicity Analysis for Time-Related Sequence Data
- Trend analysis
- Similarity search in Time-series analysis Methodologies for stream data processing and stream data systems
- Frequent pattern mining in stream data
- Sequential Pattern Mining in Data Streams
- Classification of dynamic data streams
- Class Imbalance Problem
Unit 4 Sequential Pattern Mining
- scalable methods for mining sequential patterns – GSP, SPADE
- Mining Closed Sequential Patterns
- Mining Multidimensional and Multilevel sequential Patterns. 5 12 4 Graph mining and Web mining Mining frequent subgraphs
- finding clusters
- hub and outliers in large graphs
- Graph Partitioning
- Web Mining
- Mining the web page layout structure
- mining web link structure
- mining multimedia data on the web, Automatic classification of web documents and web usage mining
- Program Name: Master of Engineering Level: PG Branch: Computer Engineering (Artificial Intelligence And Data Science) Course / Subject Code : ME01095031 Subject Name: Data Warehousing and Data Mining
Unit 5 Big Data Mining and Analytics
- Big Data: Introduction to big data: distributed file system – Big Data and its importance
- Four Vs, Drivers for Big data
- Big data analytics
- Big data applications
- Algorithms using map reduce
- Matrix-Vector Multiplication by Map Reduce
- Introduction to Hadoop architecture: Hadoop Architecture
- Hadoop Storage: HDFS
- Common Hadoop Shell commands
- Anatomy of File Write and Read.
- Secondary NameNode, and DataNode
- Hadoop MapReduce paradigm
- Map and Reduce tasks, Job, Task trackers - Cluster Setup – SSH & Hadoop Configuration – HDFS Administering –Monitoring & Maintenance
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