Data Mining & Data Warehousing
CS 503/CS 5003
Course Outcomes
CO1. Discuss data mining and knowledge discovery
CO2. Evaluate raw data to make it suitable for various data mining algorithms.
CO3. Design Multidimensional Data Model, Data Warehouse Architecture, Efficient Methods for Data Cube Computation.
CO4. Identify and apply Data mining techniques to solve real world problems.
CO5.Analyse data mining and warehousing projects in business environment.
Time & Place
Monday 12:00 - 1:00 PM
Tuesday 2:30 - 3:30 PM
Wednesday 12:00 - 1:00 PM
Saturday 12:00 - 1:00 PM
Syllabus
UNIT:1
Overview: Data warehousing, The compelling need for data warehousing, the building blocks of data warehouse, data warehouses and data marts, overview of the components, metadata in the data warehouse, trends in data warehousing, emergence of standards, OLAP Vs OLTP, data cube, multidimensional data warehouse.
UNIT:2
Introduction to Data mining, Data mining Functionalities, Data pre-processing (data summarization, data cleaning, data integration and transformation, data reduction, data discretization), Mining frequent patterns, associations, correlations (market basket analysis, the a-priori algorithm, mining various kinds of association rules, from association mining to correlation analysis).
UNIT:3
Classification: classification by decision tree induction, Rule based classification, classification by neural networks, and classification by genetic algorithm. Cluster Analysis: types of data in cluster analysis, A categorization of major clustering methods (partitioning methods, hierarchical methods), clustering high dimensional data, outlier analysis Advanced techniques: web mining, spatial mining, temporal mining.
UNIT:4
Introduction to the data warehouse project, Data warehousing implementation, Web enabled data warehouse, Data mining applications in (financial data Analysis, retail industry, telecommunication industry, Biological data analysis, intrusion detection, in other scientific applications), and Data warehouse project.