DATA WAREHOUSING AND MINING & BUSINESS INTELLIGENCE

Mumbai University-Fourth / Final Year -Semester VII Information Technology Syllabus (Revised) DATA WAREHOUSING AND MINING & BUSINESS INTELLIGENCE

DATA WAREHOUSING AND MINING & BUSINESS INTELLIGENCE
CLASS B.E. ( INFORMATION TECHNOLOGY) SEMESTER VII
HOURS PER WEEK	LECTURES	:	04
TUTORIALS	:	--
PRACTICALS	:	02
	HOURS	MARKS
EVALUATION SYSTEM:	THEORY		3	100
PRACTICAL		--	--
ORAL		--	25
TERM WORK		--	25
Prerequisite: Data Base Management System
Objective: 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 intelligence applications. 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 Mining.

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 categorical data.

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 & Prediction: What is it? Issues regarding Classification and prediction:

• 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 Databases.

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 Approaches.

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 CRM etc.

Text Books:

1. Han, Kamber, "Data Mining Concepts and Techniques", Morgan Kaufmann 2nd Edition

2. P. N. Tan, M. Steinbach, Vipin Kumar, “Introduction to Data Mining”, Pearson Education

Reference Books:

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”, Wiley India.

3. Michael Berry and Gordon Linoff “Data Mining Techniques”, 2nd Edition Wiley Publications.

4. Alex Berson and Smith, “Data Mining and Data Warehousing and OLAP”, McGraw Hill Publication.

5. E. G. Mallach, “Decision Support and Data Warehouse Systems", Tata McGraw Hill.

6. Michael Berry and Gordon Linoff “Mastering Data Mining- Art & science of CRM”, Wiley Student Edition

7. Arijay Chaudhry & P. S. Deshpande, “Multidimensional Data Analysis and Data Mining Dreamtech Press

8. Vikram Pudi & Radha Krishna, “Data Mining”, Oxford Higher Education.

9. Chakrabarti, S., “Mining the Web: Discovering knowledge from hypertext data”,

10. M. Jarke, M. Lenzerini, Y. Vassiliou, P. Vassiliadis (ed.), “Fundamentals of Data Warehouses”, Springer-Verlag, 1999.

Term Work: Term work shall consist of at least 10 experiments covering all topics Term work should consist of at least 6 programming assignments and one mini project in Business Intelligence and two assignments covering the topics of the syllabus. One written test is also to be conducted. Distribution of marks for term work shall be as follows: Attendance (Theory and Practical) 05 Marks Laboratory work (Experiments and Journal) 10 Marks

Test (at least one) 10 Marks The final certification and acceptance of TW ensures the satisfactory Performance of laboratory Work and Minimum Passing in the term work. Suggested Experiment List

1. Students can learn to use WEKA open source data mining tool and run data mining algorithms on datasets.

2. Program for Classification – Decision tree, Naïve Bayes using languages like JAVA

3. Program for Clustering – K-means, Agglomerative, Divisive using languages like JAVA

4. Program for Association Mining using languages like JAVA

5. Web mining

6. BI projects: any one of Balanced Scorecard, Fraud detection, Market Segmentation etc.

7. Using any commercial BI tool like SQLServer 2008, Oracle BI, SPSS, Clementine, and XLMiner etc.

ORAL EXAMINATION An oral examination is to be conducted based on the above syllabus.