Data Warehousing, Mining and Business Intelligence (DWMBI) SYLLABUS
Prerequisite : Data Base Management System
Today is the era characterized by Information Overload – Minimum
knowledge. Every business must rely extensively on data analysis to increase
productivity and survive competition. This course provides a comprehensive
introduction to data mining problems concepts with particular emphasis on business
The three main goals of the course are to enable students to:
1. Approach business problems data-analytically by identifying opportunities to
derive business value from data.
2. know the basics of data mining techniques and how they can be applied to extract
relevant business intelligence.
1. Introduction to Data Mining: Motivation for Data Mining, Data Mining-Definition
& Functionalities, Classification of DM systems, DM task primitives, Integration of a
Data Mining system with a Database or a Data Warehouse, Major issues in Data
2. Data Warehousing – (Overview Only): Overview of concepts like star schema, fact
and dimension tables, OLAP operations, From OLAP to Data Mining.
3. Data Preprocessing: Why? Descriptive Data Summarization, Data Cleaning:
Missing Values, Noisy Data, Data Integration and Transformation. Data Reduction:-
Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity
Reduction, Data Discretization and Concept hierarchy generation for numerical and
4. Mining Frequent Patterns, Associations, and Correlations: Market Basket
Analysis, Frequent Itemsets, Closed Itemsets, and Association Rules, Frequent
Pattern Mining, Efficient and Scalable Frequent Itemset Mining Methods, The
Apriori Algorithm for finding Frequent Itemsets Using Candidate Generation,
Generating Association Rules from Frequent Itemsets, Improving the Efficiency of
Apriori, Frequent Itemsets without Candidate Generation using FP Tree, Mining
Multilevel Association Rules, Mining Multidimensional Association Rules, From
Association Mining to Correlation Analysis, Constraint-Based Association Mining.
5. Classification and Prediction: What is it? Issues regarding Classification and
• Classification methods: Decision tree, Bayesian Classification, Rule based
• Prediction: Linear and non linear regression
Accuracy and Error measures, Evaluating the accuracy of a Classifier or Predictor.
6. Cluster Analysis: What is it? Types of Data in cluster analysis, Categories of
clustering methods, Partitioning methods – K-Means, K-Mediods. Hierarchical
Clustering- Agglomerative and Divisive Clustering, BIRCH and ROCK methods,
DBSCAN, Outlier Analysis
7. Mining Stream and Sequence Data: What is stream data? Classification, Clustering
Association Mining in stream data. Mining Sequence Patterns in Transactional
8. Spatial Data and Text Mining: Spatial Data Cube Construction and Spatial OLAP,
Mining Spatial Association and Co-location Patterns, Spatial Clustering Methods,
Spatial Classification and Spatial Trend Analysis. Text Mining Text Data Analysis
and Information Retrieval, Dimensionality Reduction for Text, Text Mining
9. Web Mining: Web mining introduction, Web Content Mining, Web Structure
Mining, Web Usage mining, Automatic Classification of web Documents.
10. Data Mining for Business Intelligence Applications: Data mining for business
Applications like Balanced Scorecard, Fraud Detection, Clickstream Mining, Market
Segmentation, retail industry, telecommunications industry, banking & finance and
1. Han, Kamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann 2nd
Free download ppts of 3rd edition of Han, Kamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann
2. P. N. Tan, M. Steinbach, Vipin Kumar, “Introduction to Data Mining”, Pearson
1. MacLennan Jamie, Tang ZhaoHui and Crivat Bogdan, “Data Mining with Microsoft
SQL Server 2008”, Wiley India Edition.
2. G. Shmueli, N.R. Patel, P.C. Bruce, “Data Mining for Business Intelligence:
Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner”,
3. Michael Berry and Gordon Linoff “Data Mining Techniques”, 2nd Edition Wiley
4. Alex Berson and Smith, “Data Mining and Data Warehousing and OLAP”, McGraw
5. E. G. Mallach, “Decision Support and Data Warehouse Systems”, Tata McGraw Hill.