M Tech (Computer Science and Applications):Data Warehousing and Data MiningThapar University
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Frequent Asked Questions
Admission to M. Tech. (Computer Science and Applications) will be open to a candidate who obtains at least 50% marks in aggregate in the qualifying examination from a recognized university.
Advanced Data Structures
Data Communication and Computer Networks
Computer Organization and Operating Systems
Computational Algorithms in Optimization
Statistical Methods and Algorithms
Database Management and Administration
Object Oriented Analysis and Design
Logic and its applications
Computer Graphics and Multimedia Technologies
Web Technologies and E-Governance
Data Warehousing and Data Mining
Introduction: Data Warehousing, Characteristics of a Data Warehouse, Data marts and Data mining.
Data Mining Techniques: A Statistical Perspective on Data Mining, Similarity Measures, Decision Trees, Neural Networks, Genetic Algorithms.
Classification: Statistical-Based Algorithms, Distance-Based Algorithms, Decision Tree-Based Algorithms, Neural Network-Based Algorithms, Rule-Based Algorithms, Combining Techniques.
Clustering: Similarity and Distance Measures, Hierarchical Algorithms, Partitional Algorithms, Clustering Large Databases, Clustering with Categorical Attributes.
Association Rules: Basic Algorithms, Parallel and Distributed Algorithms, Incremental Rules, Advanced Association Rule Techniques, Measuring the Quality of Rules.
Advanced Techniques: Mining Spatial Databases: Spatial Data Cube and OLAP, Spatial Association, Clustering and classification. Mining Text Databases: Text Data Analysis and Information Retrieval, Text Mining: Keyword-based Association and Document Classification. Mining the WEB: Mining Web’s link structure, Classification of Web pages, Web Usage Mining.
Data Warehousing: Heterogeneous information; the integration problem; the Warehouse Architecture; Data Warehousing; Warehouse DBMS. Building a Data Warehouse, Data Warehouse architectural strategies, Design considerations, Data content , metadata, distribution of data, Tools for Data Warehousing, performance considerations, Crucial decisions in Designing a Data Warehouse, various technological considerations.
Developing Data Mart based Data warehouse: Types of Data Marts, Loading a Data Mart, Metadata for a data Mart, Data Model for a Data Mart, Maintenance of a Data Mart and Performance issues
OLAP Operations: Decision support; Data Marts; OLAP vs OLTP; the Multi-Dimensional data model; Dimensional Modelling; ROLAP vs MOLAP; Star and snowflake schemas; the MOLAP cube; roll-up, slicing, and pivoting.
Laboratory Work: Developing Data warehouse and OLAP systems. Execution of different data mining algorithms using data mining tools.